Systems and methods for a compressed controller for an active exoskeleton

ABSTRACT

A system to augment motion via a battery-powered active exoskeleton boot is provided. The system can include a controller and an electric motor that generates torque about an axis of rotation of an ankle joint of the user. The controller can receive sensor data associated with activity of the exoskeleton boot during a first time interval. The controller can determine, based on the sensor data input into a model trained via a machine learning technique associated with one or more users performing one or more physical activities, one or more commands for a second time interval. The controller can transmit the one or more commands generated based on the model to the electric motor to cause the electric motor to generate torque about the axis of rotation of the ankle joint of the user in the second time interval.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims the benefit of priority to U.S.Provisional Application 63/033,562, filed on Jun. 2, 2020. The entiredisclosure of which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to the field of exoskeletons.

BACKGROUND

Exoskeletons can be worn by a user to facilitate movement of limbs ofthe user.

SUMMARY

At least one aspect of the present disclosure is directed to a system toaugment motion via a battery-powered active exoskeleton boot. The systemcan include a shin pad of an exoskeleton boot to couple to a shin of auser below a knee of the user. The system can include one or morehousings enclosing i) a controller comprising memory and one or moreprocessors, and ii) an electric motor that generates torque about anaxis of rotation of an ankle joint of the user. In embodiments, at leastone of the one or more housings is coupled to the shin pad below theknee of the user. The system can include a battery holder coupled to theshin pad. The battery holder can be configured to receive a batterymodule. The system can include an output shaft coupled to the electricmotor and extending through a bore in a housing of the one or morehousings enclosing the electric motor. The controller can receive sensordata associated with activity of the exoskeleton boot during a firsttime interval. The controller can determine, based on the sensor datainput into a model trained via a machine learning technique based onhistorical motion capture data associated with one or more usersperforming one or more physical activities, one or more commands for asecond time interval subsequent to the first time interval. Thecontroller can transmit the one or more commands generated based on themodel to the electric motor to cause the electric motor to generatetorque about the axis of rotation of the ankle joint of the user in thesecond time interval.

In embodiments, the controller can receive, via a network, the modelfrom a command modelling system that trains the model based on thehistorical motion capture data. The controller can receive historicalvideo data associated with the one or more users performing the one ormore physical activities, identify, based on historical videoinformation, one or more torque profiles corresponding to the one ormore physical activities and train, using the machine learning techniqueand based on the one or more torque profiles, the model to cause themodel to output the one or more commands responsive to the sensor data.

The system can include a command modelling system. The command modellingsystem can receive the historical motion capture data comprisinghistorical sensor data, provide, for display via a display devicecommunicatively coupled to the command modelling system, a visualindication of the historical motion capture data, receive, via a userinterface, an indication of a torque profile corresponding to the visualindication of the historical motion capture data and train, using themachine learning technique and based on the indication of the torqueprofile received via the user interface, the model to cause the model tooutput the one or more commands responsive to the sensor data. Thecommand modelling system can receive the historical motion capture datacomprising historical sensor data, provide, for display via a displaydevice communicatively coupled to the command modelling system, a visualindication of the historical motion capture data, receive, via a userinterface, an indication of a type of physical activity corresponding tothe visual indication of the historical motion capture data and train,using the machine learning technique and based on the indication of thetype of physical activity received via the user interface, the model tocause the model to output the one or more commands responsive to thesensor data.

In embodiments, the type of physical activity can include at least oneof: walking, running, standing, standing up, or sitting. The one or morephysical activities can include at least one of steady state activitiesor transient activities. The controller can determine the one or morecommands for the second time interval to match a torque profile selectedbased on the sensor data via the model. The command modelling system canreceive the historical motion capture data comprising historical sensordata, receive indications of types of physical activities correspondingto the historical motion capture data, and train, using a second machinelearning technique and based on the indications of types of physicalactivities corresponding to the historical motion capture data, a secondmodel to generate a torque profile based on second historical motioncapture data.

The command modelling system can receive the second historical motioncapture data, determine, based on the second model, one or more torqueprofiles based on the second historical motion capture data and trainthe model based on the determined one or more torque profiles and thesecond historical motion capture data to cause the model to generate theone or more commands based on the sensor data. The historical motioncapture data can correspond to data collected via the exoskeleton bootin a plurality of states comprising: an unpowered state, a partiallypowered state, and a fully powered state.

The controller can receive, via a user interface, input from the userprior to the second time interval and generate, via the model, the oneor more commands based on the input and the sensor data. The sensor datacan include at least one of ankle joint data, inertial measurement unitdata, or battery data. The motion capture data can include at least oneof inertial measurement unit data, goniometer data, infrared reflectordata, or force plate data.

In at least one aspect, a method of augmenting motion via abattery-powered active exoskeleton boot is provided. The method caninclude providing a shin pad of an exoskeleton boot for coupling to ashin of a user below a knee of the user. The method can includeproviding one or more housings enclosing i) a controller comprisingmemory and one or more processors, and ii) an electric motor thatgenerates torque about an axis of rotation of an ankle joint of theuser. In embodiments, at least one of the one or more housings iscoupled to the shin pad below the knee of the user. The method caninclude providing a battery holder coupled to the shin pad, the batteryholder to receive a battery module. The method can include providing anoutput shaft coupled to the electric motor and extending through a borein a housing of the one or more housings enclosing the electric motor.The method can include receiving, by the controller, sensor dataassociated with activity of the exoskeleton boot during a first timeinterval. The method can include determining, by the controller, basedon the sensor data input into a model trained via a machine learningtechnique based on historical motion capture data associated with one ormore users performing one or more physical activities, one or morecommands for a second time interval subsequent to the first timeinterval. The method can include transmitting, by the controller, theone or more commands generated based on the model to the electric motorto cause the electric motor to generate torque about the axis ofrotation of the ankle joint of the user in the second time interval.

In embodiments, the method can include receiving, by a command modellingsystem, historical video data associated with the one or more usersperforming the one or more physical activities. The method can includeidentifying, by the command modelling system based on the historicalvideo data, one or more torque profiles corresponding to the one or morephysical activities. The method can include training, by the commandmodelling system, using the machine learning technique and based on theone or more torque profiles, the model to cause the model to output theone or more commands responsive to the sensor data. The method caninclude receiving, by a command modelling system, the historical motioncapture data comprising historical sensor data. The method can includeproviding, by the command modelling system, for display via a displaydevice communicatively coupled to the command modelling system, a visualindication of the historical motion capture data. The method can includereceiving, by the command modelling system via a user interface, anindication of a torque profile corresponding to the visual indication ofthe historical motion capture data. The method can include training, bythe command modelling system, using the machine learning technique andbased on the indication of the torque profile received via the userinterface, the model to cause the model to output the one or morecommands responsive to the sensor data.

In embodiments, the method can include receiving, by a command modellingsystem, the historical motion capture data comprising historical sensordata. The method can include providing, by the command modelling system,for display via a display device communicatively coupled to the commandmodelling system, a visual indication of the historical motion capturedata. The method can include receiving, by the command modelling systemvia a user interface, an indication of a type of physical activitycorresponding to the visual indication of the historical motion capturedata. The method can include training, by the command modelling system,using the machine learning technique and based on the indication of thetype of physical activity received via the user interface, the model tocause the model to output the one or more commands responsive to thesensor data.

The method can include determining, by the controller, the one or morecommands for the second time interval to match a torque profile selectedbased on the sensor data via the model. The method can includereceiving, by the controller via a user interface, input from the userprior to the second time interval. The method can include generating, bythe controller via the model, the one or more commands based on theinput and the sensor data.

Those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the devices and/orprocesses described herein, as defined solely by the claims, will becomeapparent in the detailed description set forth herein and taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of one or more implementations of the subject matterdescribed in this specification are set forth in the accompanyingdrawings and the description below. Other features, aspects, andadvantages of the subject matter will become apparent from thedescription, the drawings, and the claims.

FIG. 1 illustrates a schematic diagram of an exoskeleton, according toan embodiment.

FIG. 2 illustrates a schematic diagram of an exoskeleton, according toan embodiment.

FIG. 3 illustrates a schematic diagram of an exoskeleton, according toan embodiment.

FIG. 4 illustrates a schematic diagram of an exoskeleton, according toan embodiment.

FIG. 5 illustrates a schematic diagram of the exoskeleton and internalparts, according to an embodiment.

FIG. 6 illustrates a side view of an exoskeleton, according to anembodiment.

FIG. 7 illustrates a schematic diagram of an exoskeleton, according toan embodiment.

FIG. 8 illustrates a schematic diagram of an exoskeleton and internalparts, according to an embodiment.

FIG. 9 illustrates a schematic diagram of an exoskeleton and internalparts, according to an embodiment.

FIG. 10 illustrates a side view of an exoskeleton, according to anembodiment.

FIG. 11 illustrates a side view of an exoskeleton, according to anembodiment.

FIG. 12 illustrates a method of augmenting user motion, according to anembodiment.

FIG. 13 illustrates a block diagram of an architecture for a computingsystem employed to implement various elements of the system and methodsdepicted in FIGS. 1-21, according to an embodiment.

FIG. 14 is a block diagram of a system for augmenting motion via abattery-powered active exoskeleton boot in accordance with anillustrative embodiment;

FIG. 15 illustrates a method of augmenting motion via a battery-poweredactive exoskeleton boot, according to an embodiment.

FIG. 16 is a block diagram of a system for training a model to generateone or more commands in accordance with an illustrative embodiment.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

This disclosure relates generally to performance enhancing wearabletechnologies. Particularly, this disclosure relates to apparatuses,systems, and methods for an active exoskeleton with a local battery. Thelocal battery can include an onboard power source that is used to powerelectronics and one or more actuators.

I. Exoskeleton Overview

Exoskeletons (e.g., battery-powered active exoskeleton, battery-poweredactive exoskeleton boot, lower limb exoskeleton, knee exoskeleton, orback exoskeleton) can include devices worn by a person to augmentphysical abilities. Exoskeletons can be considered passive (e.g., notrequiring an energy source such as a battery) or active (e.g., requiringan energy source to power electronics and usually one or manyactuators). Exoskeletons may be capable of providing large amounts offorce, torque and/or power to the human body in order to assist withmotion.

Exoskeletons can transfer energy to the user or human. Exoskeletons maynot interfere with the natural range of motion of the body. For example,exoskeletons can allow a user to perform actions (e.g., walking,running, reaching, or jumping) without hindering or increasing thedifficulty of performing these actions. Exoskeletons can reduce thedifficulty of performing these actions by reducing the energy or effortthe user would otherwise exert to perform these actions. Exoskeletonscan convert the energy into useful mechanical force, torque, or power.Onboard electronics (e.g., controllers) can control the exoskeleton.Output force and torque sensors can also be used to make controllingeasier.

FIG. 1 illustrates a schematic diagram of an exoskeleton 100. Theexoskeleton 100 can be referred to as a lower limb exoskeleton, lowerlimb exoskeleton assembly, lower limb exoskeleton system, ankleexoskeleton, ankle foot orthosis, knee exoskeleton, hip exoskeleton,exoskeleton boot, or exoboot. The exoskeleton 100 can include a waterresistant active exoskeleton boot. For example, the exoskeleton 100 canresist the penetration of water into the interior of the exoskeleton100. The exoskeleton 100 can include a water resistant activeexoskeleton boot. For example, the exoskeleton 100 can be impervious toliquids (e.g., water) and non-liquids (e.g., dust, dirt, mud, sand, ordebris). The exoskeleton 100 can remain unaffected by water or resistthe ingress of water, such as by decreasing a rate of water flow intothe interior of the exoskeleton 100 to be less than a target rateindicative of being water resistant or waterproof. For example, theexoskeleton 100 can operate in 3 feet of water for a duration of 60minutes. The exoskeleton 100 can have an ingress protection rating (IP)rating of 68. The exoskeleton 100 can have a National ElectricalManufacturer Association (NEMA) rating of 4×, which can indicate thatthe exoskeleton 100 has a degree of protection with respect to harmfuleffects on the equipment due to the ingress of water (e.g., rain, sleet,snow, splashing water, and hose directed water), and that theexoskeleton can be undamaged by the external formation of ice on theenclosure.

The exoskeleton 100 can include a shin pad 125 (e.g., shin guard). Theshin pad 125 can be coupled to a shin of a user below a knee of theuser. The shin pad 125 can be coupled to the shin of the user to providesupport. The shin pad 125 can include a piece of equipment to protectthe user from injury. For example, the shin pad 125 can protect thelower extremities of the user from external impact. The shin pad 125 caninterface with the shin of the user. The shin pad 125 can include a band(e.g., adjustable band) configured to wrap around the shin of the user.The shin pad 125 can secure the upper portion of the exoskeleton 100 tothe body of the user. The shin pad 125 can secure or help secure theexoskeleton 100 to the shin, leg, or lower limb of the user. The shinpad 125 can provide structural integrity to the exoskeleton 100. Theshin pad 125 can support other components of the exoskeleton 100 thatcan be coupled to the shin pad 125. The shin pad 125 can be made oflightweight, sturdy, and/or water resistant materials. For example, theshin pad 125 can be made of plastics, aluminum, fiberglass, foam rubber,polyurethane, and/or carbon fiber.

The exoskeleton 100 can include one or more housings 105. At least oneof the one or more housings 105 can be coupled to the shin pad 125 belowthe knee of the user. The shin pad 125 can be coupled to the at leastone housing via a shin lever. The shin lever can extend from the atleast one housing to the shin pad 125. The shin lever can include amechanical structure that connects the shin pad 125 to a chassis. Thechassis can include a mechanical structure that connects staticcomponents.

The one or more housings 105 can enclose electronic circuitry (e.g.,electronic circuitry 505). The one or more housings 105 can encapsulatesome or all the electronics of the exoskeleton 100. The one or morehousings 105 can include an electronics cover (e.g., case). The one ormore housings 105 can enclose an electric motor (e.g., motor 330). Theelectric motor can generate torque about an axis of rotation of an anklejoint of the user. The ankle joint can allow for dorsiflexion and/orplantarflexion of the user's foot. The exoskeleton 100 can include anankle joint component 120 that rotates about the axis of rotation theankle joint. The ankle joint component 120 can be positioned around oradjacent to the ankle joint.

The exoskeleton 100 can include a rotary encoder 155 (e.g., shaftencoder, first rotary encoder, or motor encoder). The rotary encoder 155can be enclosed within the one or more housings 105. The rotary encoder155 can measure an angle of the electric motor. The angle of theelectric motor can be used by the controller to determine an amount oftorque applied by the exoskeleton 100. For example, the angle of theelectric motor can correspond to an amount of torque applied by theexoskeleton 100. An absolute angle of the electric motor can correspondto an amount of torque applied by the exoskeleton 100. The rotaryencoder 155 can include an inductive encoder. The ankle joint component120 can be actuated by a motor (e.g., electric motor). The rotaryencoder 155 can include a contactless magnetic encoder or an opticalencoder.

The exoskeleton 100 can include a second rotary encoder 160 (e.g., ankleencoder). The second rotary encoder 160 can measure an angle of theankle joint. The angle of the ankle joint can be used by the controllerto determine an amount of torque applied by the exoskeleton 100. Thesecond rotary encoder 160 can include a first component enclosed in theone or more housings 105 and in communication with the electroniccircuitry 505. The second rotary encoder 160 can include a secondcomponent located outside the one or more housings 105 and configured tointeract with the first component. The second rotary encoder 160 caninclude a contactless magnetic encoder, a contactless inductive encoder,or an optical encoder. The second rotary encoder 160 can detect theangle of the ankle joint while the rotary encoder 155 can detect theangle of the electric motor. The angle of the electric motor can bedifferent from the angle of the ankle joint. The angle of the electricmotor can be independent of the angle of the ankle joint. The angle ofthe ankle joint can be used to determine an output (e.g., torque) of theelectric motor. The ankle joint component 120 can be coupled to thesecond rotary encoder 160.

The one or more housings 105 can encapsulate electronics that are partof the exoskeleton 100. The one or more housings 105 can form a fittedstructure (e.g., clamshell structure) to enclose the electroniccircuitry and the electric motor. The fitted structure can be formedfrom two or more individual components. The individual components of thefitted structure can be joined together to form a single unit. The oneor more housings 105 can be formed of plastic or metal (e.g., aluminum).An adhesive sealant can be placed between individual components of thefitted structure and under the electronics cover. A gasket can be placedbetween individual components of the fitted structure and under theelectronics cover. The gasket can be placed in the seam between theindividual components of the fitted structure.

A sealant 165 can be placed in contact with the one or more housings 105to close the one or more housings 105 and prevent an ingress of waterinto the one or more housings 105. The sealant 165 used to close the oneor more housings 105 can include an adhesive sealant (e.g., super glue,epoxy resin, or polyvinyl acetate). The adhesive sealant can include asubstance used to block the passage of fluids through the surface orjoints of the one or more housings 105. The sealant 165 used to closethe one or more housings 105 can include epoxy. The sealant 165 canpermanently seal or close the one or more housings 105. For example, thesealant 165 can seal or close the one or more housings 105 such that theone or more housings are not removably attached to one another.

The exoskeleton 100 can couple with a boot 110. For example, theexoskeleton 100 can be attached to the boot 110. The boot 110 can beworn by the user. The boot 110 can be connected to the exoskeleton 100.The exoskeleton 100 can be compatible with different boot shapes andsizes.

The exoskeleton 100 can include an actuator 130 (e.g., actuator leverarm, or actuator module). The actuator 130 can include one or more ofthe components in the exoskeleton 100. For example, the actuator 130 caninclude the one or more housings 105, the footplate 115, the ankle jointcomponent 120, the actuator belt 135, and the post 150, while excludingthe boot 110. The boot 110 can couple the user to the actuator 130. Theactuator 130 can provide torque to the ground and the user.

The exoskeleton 100 can include a footplate 115 (e.g., carbon insert,carbon shank). The footplate 115 can include a carbon fiber structurelocated inside of the sole of the boot 110. The footplate 115 can bemade of a carbon-fiber composite. The footplate 115 can be inserted intothe sole of the boot 110. The footplate 115 can be used to transmittorque from the actuator 130 to the ground and to the user. Thefootplate 115 can be located in the sole of the exoskeleton 100. Thisfootplate 115 can have attachment points that allow for the connectionof the exoskeleton's mechanical structure. An aluminum insert withtapped holes and cylindrical bosses can be bonded into the footplate115. This can create a rigid mechanical connection to the largelycompliant boot structure. The bosses provide a structure that can beused for alignment. The footplate 115 can be sandwiched between twostructures, thereby reducing the stress concentration on the part. Thisdesign can allow the boot to function as a normal boot when there is noactuator 130 attached.

The exoskeleton 100 can include an actuator belt 135 (e.g., beltdrivetrain). The actuator belt 135 can include a shaft that is driven bythe motor and winds the actuator belt 135 around itself. The actuatorbelt 135 can include a tensile member that is pulled by the spool shaftand applies a force to the ankle lever. Tension in the actuator belt 135can apply a force to the ankle lever. The exoskeleton 100 can include anankle lever. The ankle lever can include a lever used to transmit torqueto the ankle. The exoskeleton 100 can be used to augment the anklejoint.

The exoskeleton 100 can include a power button 140 (e.g., switch, powerswitch). The power button 140 can power the electronics of theexoskeleton 100. The power button 140 can be located on the exterior ofthe exoskeleton 100. The power button 140 can be coupled to theelectronics in the interior of the exoskeleton 100. The power button 140can be electrically connected to an electronic circuit. The power button140 can include a switch configured to open or close the electroniccircuit. The power button 140 can include a low-power, momentarypush-button configured to send power to a microcontroller. Themicrocontroller can control an electronic switch.

The exoskeleton 100 can include a battery holder 170 (e.g., chargingstation, dock). The battery holder 170 can be coupled to the shin pad125. The battery holder 170 can be located below the knee of the user.The battery holder 170 can be located above the one or more housings 105enclosing the electronic circuitry. The exoskeleton 100 can include abattery module 145 (e.g., battery). The battery holder 170 can include acavity configured to receive the battery module 145. A coefficient offriction between the battery module 145 and the battery holder 170 canbe established such that the battery module 145 is affixed to thebattery holder 170 due to a force of friction based on the coefficientof friction and a force of gravity. The battery module 145 can beaffixed to the battery holder 170 absent a mechanical button ormechanical latch. The battery module 145 can be affixed to the batteryholder 170 via a lock, screw, or toggle clamp. The battery holder 170and the battery module 145 can be an integrated component (e.g.,integrated battery). The integrated battery can be supported by a frameof the exoskeleton 100 as opposed to having a separated enclosure. Theintegrated battery can include a charging port. For example, thecharging port can include a barrel connector or a bullet connector. Theintegrated battery can include cylindrical cells or prismatic cells.

The battery module 145 can power the exoskeleton 100. The battery module145 can include one or more electrochemical cells. The battery module145 can supply electric power to the exoskeleton 100. The battery module145 can include a power source (e.g., onboard power source). The powersource can be used to power electronics and one or more actuators. Thebattery module 145 can include a battery pack. The battery pack can becoupled to the one or more housings 105 below a knee of the user. Thebattery pack can include an integrated battery pack. The integratedbattery pack can remove the need for power cables, which can reduce thesnag hazards of the system. The integrated battery pack can allow thesystem to be a standalone unit mounted to the user's lower limb. Thebattery module 145 can include a battery management system 324 toperform various operations. For example, the system can optimize theenergy density of the unit, optimize the longevity of the cells, andenforce safety protocols to protect the user.

The battery module 145 can include a removable battery. The batterymodule 145 can be referred to as a local battery because it is locatedon the exoboot 100 (e.g., on the lower limb or below the knee of theuser), as opposed to located on a waist or back of the user. The batterymodule 145 can include a weight-mounted battery, which can refer to thebattery being held in place on the exoboots 100 via gravity andfriction, as opposed to a latching mechanism. The battery module 145 caninclude a water resistant battery or a waterproof battery. Theexoskeleton 100 and the battery module 145 can include water resistantconnectors.

The battery module 145 can include a high-side switch (e.g., positivecan be interrupted). The battery module 145 can include a ground that isalways connected. The battery module 145 can include light emittingdiodes (LEDs). For example, the battery module 145 can include threeLEDs used for a user interface. The LEDs can be visible from one lens sothat the LEDs appear as one multicolor LED. The LEDs can blink invarious patterns and/or colors to communicate a state of the batterymodule 145 (e.g., fully charged, partially charged, low battery, orerror).

The exoskeleton 100 can include a post 150. The post 150 can include amechanical structure that connects to the boot 110. The post 150 cancouple the ankle joint component 120 with the footplate 115. The post150 can be attached at a first end to the footplate 115. The post 150can be attached at a second end to the ankle joint component 120. Thepost 150 can pivot about the ankle joint component 120. The post 150 caninclude a mechanical structure that couples the footplate 115 with theankle joint component 120. The post 150 can include a rigid structure.The post 150 can be removably attached to the footplate 115. The post150 can be removably attached to the ankle joint component 120. Forexample, the post 150 can be disconnected from the ankle joint component120.

The exoskeleton 100 can include a rugged system used for field testing.The exoskeleton 100 can include an integrated ankle lever guard (e.g.,nested lever). The exoskeleton 100 can include a mechanical shield toguard the actuator belt 135 and ankle lever transmission from theenvironment. The housing structure of the system can extend to outlinethe range of travel of the ankle lever (e.g., lever arm 1140) on thelateral and medial side.

II. Active Exoskeleton with Local Battery

Exoskeletons 100 can transform an energy source into mechanical forcesthat augment human physical ability. Exoskeletons 100 can have uniquepower requirements. For example, exoskeletons 100 can use non-constantpower levels, such as cyclical power levels with periods of high power(e.g., 100 to 1000 Watts) and periods of low or negative power (e.g., 0Watts). Peaks in power can occur once per gait cycle. Batteriesconfigured to provide power to the exoskeleton 100 can be the source ofvarious issues. For example, batteries located near the waist of a usercan require exposed cables that extend from the battery to the lowerlimb exoskeleton. These cables can introduce snag hazards, make thedevice cumbersome, and add mass to the system. Additionally, long cableswith high peak power can result in excess radio emissions and highervoltage drops during high current peaks. Thus, systems, methods andapparatus of the present technical solution provide an exoskeleton witha local battery that can perform as desired without causing snaghazards, power losses, and radio interference. Additionally, the batterycan be located close to the knee such that the mass felt by the user isreduced as compared to a battery located close the foot of the user.

FIG. 2 illustrates a schematic diagram of the exoskeleton 100. Theexoskeleton 100 includes the one or more housings 105, the boot 110 thefootplate 115, the ankle joint component 120, shin pad 125, the actuator130, the actuator belt 135, the power button 140, the battery module145, the post 150, the rotary encoder 155, and the second rotary encoder160. The battery module 145 can be inserted into the exoskeleton 100.The battery module 145 can include a sealed battery. The battery module145 can coupled with the exoskeleton 100 via a waterproof or waterresistant connection. The battery module 145 can connect locally (e.g.,proximate) to the exoskeleton 100 such that a wire is not needed to runfrom the battery module 145 to the electronics.

The battery module 145 can be removably affixed to the battery holder170. For example, the battery module 145 can slide in and out of thebattery holder 170. By removably affixing the battery module 145 to thebattery holder 170, the battery module 145 can be replaced with anotherbattery module 145, or the battery module 145 can be removed forcharging. The battery module 145 can include a first power connector 205that electrically couples to a second power connector 210 located in thebattery holder 170 while attached to the battery holder 170 to provideelectric power to the electronic circuitry and the electric motor. Thefirst power connector 205 and the second power connector 210 can couple(e.g., connect) the battery module 145 with the electronic circuitry.The first power connector 205 and the second power connector 210 cancouple the battery module 145 with the one or more housings 105. Thefirst power connector 205 can be recessed in the battery module 145 toprotect the first power connector 205 from loading and impacts. Thefirst power connector 205 and the second power connector 210 can includewires (e.g., two wires, three wires, or four wires). The battery module145 can communicate with the electronic circuitry via the first powerconnector 205 and the second power connector 210. The first powerconnector 205 and the second power connector 210 can include an exposedconnector.

The geometry of the battery module 145 can allow for storage and packingefficiency. The battery module 145 can include a gripping element toallow for ergonomic ease of removal and insertion of the battery module145 into the battery holder 170. The battery module 145 can be made oflightweight plastics or metals. The battery module 145 can be made ofheat insulating materials to prevent heat generated by the battery cells305 from reaching the user. One or more faces of the battery module 145can be made of metal to dissipate heat.

The exoskeleton 100 can communicate with the battery module 145 duringoperation. The exoskeleton 100 can use battery management systeminformation to determine when safety measures will trigger. For example,during a high current peak (e.g., 15 A) or when the temperature is neara threshold, the power output can be turned off. The exoskeleton 100 cantemporarily increase safety limits for very specific use cases (e.g.,specific environmental conditions, battery life). The battery module 145can prevent the exoskeleton 100 from shutting down by going into a lowpower mode and conserving power. The exoskeleton 100 can put the batterymodule 145 in ship mode if a major error is detected and the exoskeleton100 wants to prevent the user from power cycling. The battery managementsystem 324 can be adapted to support more or less series cells, parallelcells, larger capacity cells, cylindrical cells, different lithiumchemistries, etc.

FIG. 3 illustrates a schematic diagram of an exoskeleton 100. Theexoskeleton 100 can include a motor 330. The motor 330 can generatetorque about an axis of rotation of an ankle joint of the user. Theexoskeleton 100 can include the battery module 145. The exoskeleton 100can include a computing system 300. The exoskeleton 100 can include oneor more processors 302, memory 304, and one or more temperature sensors106 (e.g., thermocouples). The one or more processors 302, memory 304,and one or more temperature sensor 106 can be located within thecomputing system 300. In some cases, the computing system 300 caninclude the batter balancer 308 as opposed to the battery module 145.

The one or more processors 302 can receive data corresponding to aperformance of the battery module 145. The data can include one or moreof a temperature, current, voltage, battery percentage, internal stateor firmware version. The one or more processors 302 can determine, basedon a safety policy, to trigger a safety action. The safety policy caninclude triggering the safety action if a threshold temperature, voltageor battery percentage is crossed. For example, the safety policy caninclude triggering the safety action if a temperature of one or more ofthe plurality of battery cells 305 is higher than a thresholdtemperature. The safety policy can include triggering the safety actionif a battery percentage of the battery module 145 is below a thresholdbattery percentage. The safety policy can include triggering the safetyaction if a measured temperature is higher than the thresholdtemperature. The measured temperature can include the temperature of theprinted circuit board and battery cells 305. The measured temperaturecan include the temperature of the printed circuit board and batterycells 305 measured in two locations. The safety policy can includetriggering the safety action if a measured voltage is higher than thethreshold voltage.

The one or more processors 302 can instruct, based on the safety action,the electronic circuitry to adjust delivery of power from the batterymodule 145 to the electric motor to reduce an amount of torque generatedabout the axis of rotation of the ankle joint of the user. The safetyaction can include lowering or reducing the amount of torque generatedabout the axis of rotation of the ankle joint of the user. The safetyaction can include increasing the amount of torque generated about theaxis of rotation of the ankle joint of the user.

The one or more temperature sensors 306 can be placed between theplurality of battery cells 305 to provide an indication of a temperaturebetween the plurality of battery cells 305. A temperature sensor of theone or more temperature sensors 306 can be mounted on the printedcircuit board to measure a temperature of the printed circuit board. Theelectronic circuitry 505 can control the delivery of power from thebattery module 145 to the electric motor based at least in part on theindication of the temperature between the plurality of battery cells 305or the temperature of the printed circuit board.

The one or more battery balancers 308 can be configured to activelytransfer energy from a first battery cell 305 of the plurality ofbattery cells 305 to a second battery cell 305 of the plurality ofbattery cells 305 having less charge than the first battery cell 305. Asignal trace 1210 can electrically connect the plurality of batterycells 305 to the one or more battery balancers 308. The signal trace1210 can be located on the printed circuit board.

The exoskeleton 100 can include the battery module 145. The batterymodule 145 can include a plurality of battery cells 305, one or moretemperature sensors 306, one or more battery balancers 308, and abattery management system 324. The battery management system 324 canperform various operations. For example, the battery management system324 can optimize the energy density of the unit, optimize the longevityof the cells 305, and enforce the required safety to protect the user.The battery management system 324 can go into ship mode by electricallydisconnecting the battery module 145 from the rest of the system tominimize power drain while the system is idle. The battery managementsystem 324 can go into ship mode if a major fault is detected. Forexample, if one or more of the plurality of battery cells 305self-discharge at a rate higher than a threshold, the battery managementsystem 324 can re-enable the charging port.

While these components are shown as part of the exoskeleton 100, theycan be located in other locations such as external to the exoskeleton100. For example, the battery management system 324 or the computingsystem 300 can be located external to the exoskeleton 100 for testingpurposes.

FIG. 4 illustrates a schematic diagram of the exoskeleton 100. Theexoskeleton 100 can include the one or more housings 105, the footplate115, the ankle joint component 120, shin pad 125, the actuator 130, theactuator belt 135, the post 150, the rotary encoder 155, the secondrotary encoder 160, and the sealant 165 as described above. The one ormore housings 105 can be coupled to the shin pad 125. The post 150 cancouple the ankle joint component 120 with the footplate 115. Theactuator 130 can include the one or more housings 105, the footplate115, the ankle joint component 120, the actuator belt 135, and the post150. The rotary encoder 155 can measure an angle of the electric motor.The second rotary encoder 160 can measure an angle of the ankle joint.The sealant 165 can be placed in contact with the one or more housings105 to close the one or more housings 105 and prevent an ingress ofwater into the one or more housings 105.

FIG. 5 illustrates a schematic diagram of the exoskeleton 100 andinternal parts. The exoskeleton 100 can include the one or more housings105, the ankle joint component 120, the actuator 130, the power button140, the rotary encoder 155, the second rotary encoder 160, and thesealant 165 as described above. The internal parts can includeelectronic circuitry 505 (e.g., electronic circuit, circuitry,electronics). The electronic circuitry 505 can include individualelectronic components (e.g., resistors, transistors, capacitors,inductors, diodes, processors, or controllers). The power button 140 canbe electrically connected to the electronic circuitry 505. Theelectronic circuitry 505 can be located behind the electric motor. Theelectronic circuitry 505 can include the main electronics board. Therotary encoder 155 can be located between the motor and electroniccircuitry 505. The electronic circuitry 505 can control delivery ofpower from the battery module 145 to the electric motor to generatetorque about the axis of rotation of the ankle joint of the user.

FIG. 6 illustrates a side view of the exoskeleton 100. The exoskeleton100 can include the one or more housings 105, ankle joint component 120,the actuator 130, the rotary encoder 155, the second rotary encoder 160,the sealant 165, and electronic circuitry 505 as described above. Theexoskeleton 100 can include an output shaft 605 (e.g., motor rotor,spool shaft, pinion gear, spur gear, or toothed pulley). The outputshaft 605 can be coupled to the electric motor. The output shaft 605 canextend through a bore 610 in a housing of the one or more housings 105enclosing the electric motor. The bore 610 can receive the output shaft605. An encoder chip can be located on the electronics board on a firstside of the electric motor. The encoder chip can measure the angularposition of the rotary encoder 155. The exoskeleton 100 can include atransmission (e.g., gearbox) configured to couple the output shaft 605to the electric motor. The transmission can include a machine in a powertransmission system. The transmission can provide controlled applicationof power. The output shaft 605 can be integrated into the motor rotor.The output shaft 605 can be part of a mechanism (e.g., gears, belts,linkage, or change). An ankle shaft can extend through the second rotaryencoder 160 which can increase the structural integrity of theexoskeleton 100.

The exoskeleton 100 can include a first component of the fittedstructure 615 (e.g., first clamshell structure). The exoskeleton 100 caninclude a second component of the fitted structure 620 (e.g., secondclamshell structure). The first component of the fitted structure 615can be coupled with the second component of the fitted structure 620.The first component of the fitted structure 615 can be attached to thesecond component of the fitted structure 620 via the sealant 165 (e.g.,adhesive sealant). The first component of the fitted structure 615 canbe coupled to the second component of the fitted structure 620 such thatthe fitting prevents or decreases a rate of water flow into the interiorof the exoskeleton 100. The fitted structure can include two or morecomponents such that the assembly components prevents or decreases arate of water flow into the interior of the exoskeleton 100. The firstcomponent of the fitted structure 615 and the second component of thefitted structure 620 can be stationary components. The number ofindividual components of the fitted structure can be minimized todecrease the number of possible entry points for water to enter theexoskeleton 100. The possible entry points can include seams and/ormoving parts of the exoskeleton 100. The seams can be permanently sealedvia the sealant 165.

An adhesive sealant (e.g., super glue, epoxy resin, or polyvinylacetate) can be placed between the first component of the fittedstructure 615 and the second component of the fitted structure 620. Theadhesive sealant can prevent or decrease the rate of water flow throughthe seam between the first component of the fitted structure 615 and thesecond component of the fitted structure 620 into the interior of theexoskeleton 100. The adhesive sealant can be placed under theelectronics cover. The adhesive sealant can prevent or decrease the rateof water flow through the seam between the electronics cover and theexoskeleton one or more housings 105 into the interior of theexoskeleton 100.

A gasket can be placed between the first component of the fittedstructure 615 and the second component of the fitted structure 620. Thegasket can be placed in the seam between the first component of thefitted structure 615 and the second component of the fitted structure620. The gasket can prevent or decrease the rate of water flow throughthe seam between the first component of the fitted structure 615 and thesecond component of the fitted structure 620.

FIG. 7 illustrates a schematic diagram of the exoskeleton 100. Theexoskeleton 100 can include the one or more housings 105, the footplate115, the ankle joint component 120, the shin pad 125, the actuator 130,the post 150, the rotary encoder 155, the second rotary encoder 160, andthe sealant 165 as described above. The one or more housings 105 can becoupled to the shin pad 125. The post 150 can couple the ankle jointcomponent 120 with the footplate 115. The actuator 130 can include theone or more housings 105, the footplate 115, the ankle joint component120, and the post 150. The rotary encoder 155 can measure an angle ofthe electric motor. The second rotary encoder 160 can measure an angleof the ankle joint.

FIG. 8 and FIG. 9 illustrate schematic diagrams of the exoskeleton 100and internal parts. The exoskeleton 100 can include the one or morehousings 105, the footplate 115, the ankle joint component 120, shin pad125, the actuator 130, the post 150, the rotary encoder 155, the secondrotary encoder 160, the sealant 165, and electronic circuitry 505 asdescribed above. The internal parts can include an electronic circuit(e.g., circuitry). The electronic circuit can include individualelectronic components (e.g., resistors, transistors, capacitors,inductors, diodes, processors, or controllers). The motor rotor can beconnected to the output shaft 605.

FIG. 10 illustrates a side view of the exoskeleton 100. The exoskeleton100 can include the one or more housings 105, the actuator 130, therotary encoder 155, the second rotary encoder 160, and the sealant 165,the output shaft 605, and the bore 610 as described above. Theexoskeleton 100 can include an output shaft 605 (e.g., motor rotor). Theoutput shaft 605 can be coupled to the electric motor. The output shaft605 can extend through a bore 610 in a housing of the one or morehousings 105 enclosing the electric motor. The bore 610 can receive theoutput shaft 605. A magnet can be located on a first side of theelectric motor. An encoder chip can be located on the electronics boardon the first side of the electric motor. The encoder chip can measurethe angular position of the rotary encoder 155. An ankle shaft canextend through the second rotary encoder 160 which can increase thestructural integrity of the exoskeleton 100. The exoskeleton 100 caninclude a transmission (e.g., gearbox) configured to couple the outputshaft 605 to the electric motor. The transmission can include a machinein a power transmission system. The transmission can provide controlledapplication of power.

FIG. 11 illustrates a side view of an exoskeleton 100. The exoskeleton100 can include a motor 1105 (e.g., electric motor), a motor timingpulley 1110 (e.g., timing pulley), a motor timing belt 1115 (e.g.,timing belt), the second rotary encoder 160 (e.g., an ankle encoder PCB,ankle encoder printed circuit board, second rotary encoder PCB, or ankleencoder), an ankle shaft 1125, a motor encoder magnet 1130, a motorencoder 1135, a lever arm 1140 (e.g., ankle lever), and an ankle encodermagnet 1145. The ankle shaft 1125 can extend through the second rotaryencoder 160 to increase the structural integrity of the exoskeleton 100.The motor timing belt 1115 can be coupled to a sprocket 1150. Thesprocket 1150 can be coupled with a spool. The motor encoder magnet 1130can be located on the first side of the electric motor.

FIG. 12 illustrates a method 1200 of augmenting user motion. The method1200 can include providing, to a user, a battery-powered activeexoskeleton boot (BLOCK 1205). The battery-powered active exoskeletonboot can include a shin pad to be coupled to a shin of a user below aknee of the user. The battery-powered active exoskeleton boot caninclude one or more housings enclosing electronic circuitry and anelectric motor that can generate torque about an axis of rotation of anankle joint of the user. At least one of the one or more housings can becoupled to the shin pad below the knee of the user. The battery-poweredactive exoskeleton boot can include a battery holder coupled to the shinpad. The battery holder can be located below the knee of the user andabove the one or more housings enclosing the electronic circuitry. Thebattery-powered active exoskeleton boot can include a battery moduleremovably affixed to the battery holder. The battery module can includea first power connector that electrically couples to a second powerconnector located in the battery holder while attached to the batteryholder to provide electric power to the electronic circuitry and theelectric motor. The battery-powered active exoskeleton boot can includean output shaft coupled to the electric motor and extending through abore in a housing of the one or more housings enclosing the electricmotor. The electronic circuitry can control delivery of power from thebattery module to the electric motor to generate torque about the axisof rotation of the ankle joint of the user.

In some embodiments, the first power connector includes a bladeconnector. The second power connector can include a receptacleconfigured to receive the blade connector absent an exposed cable. Thebattery module can include a plurality of battery cells 305. The batterymodule can include a printed circuit board soldered to the plurality ofbattery cells 305. The battery module can include one or more batterybalancers configured to actively transfer energy from a first batterycell 305 of the plurality of battery cells 305 to a second battery cell305 of the plurality of battery cells 305 having less charge than thefirst battery cell 305. The battery module can include a signal trace,on the printed circuit board, that electrically connects the pluralityof battery cells 305 to the one or more battery balancers.

In some embodiments, the method 1200 includes providing, via a serialdata communication port of the first power connector, at least one ofbattery state data, a battery test function, a smart charging function,or a firmware upgrade. The battery state data can include the health ofthe battery module. The battery test function can include probing thebattery module. The smart charging function can include using a highvoltage to charge the battery module. A pin of the first power connectorthat provides serial data can be further configured to receive a voltageinput greater than or equal to a threshold to wake up a batterymanagement system of the battery module.

The method 1200 can include receiving data corresponding to batterymodule performance (BLOCK 1210). For example, the method 1200 caninclude receiving, by one or more processors of the battery-poweredactive exoskeleton boot, data corresponding to a performance of thebattery module, the data comprising one or more of a temperature,current, voltage, battery percentage. For example, the data can includea temperature from one or more temperature sensors of the computingsystem. The data can include a temperature from one or more temperaturesensors of the battery module.

The method 1200 can include determining to trigger a safety action(BLOCK 1215). For example, the method 1200 can include determining, bythe one or more processors, based on a safety policy, to trigger asafety action. The safety policy can include triggering the safetyaction if a threshold temperature, voltage or battery percentage iscrossed. For example, the safety policy can include triggering thesafety action if a temperature of one or more of the plurality ofbattery cells 305 is higher than a threshold temperature. The safetypolicy can include triggering the safety action if a battery percentageof the battery module is below a threshold battery percentage. Themeasured temperature can include the temperature of the printed circuitboard and battery cells 305. The measured temperature can include thetemperature of the printed circuit board and battery cells 305 measuredin two locations. The safety policy can include triggering the safetyaction if a measured voltage is higher than the threshold voltage.

The method 1200 can include instructing circuitry to adjust powerdelivery (BLOCK 1220). For example, the method 1200 can includeinstructing, by the one or more processors, based on the safety action,the electronic circuitry to adjust delivery of power from the batterymodule to the electric motor to reduce an amount of torque generatedabout the axis of rotation of the ankle joint of the user. The safetyaction can include lowering or reducing the amount of torque generatedabout the axis of rotation of the ankle joint of the user. The safetyaction can include increasing the amount of torque generated about theaxis of rotation of the ankle joint of the user.

FIG. 13 illustrates a block diagram of an architecture for a computingsystem employed to implement various elements of the system and methodsdepicted in FIGS. 1-21, according to an embodiment. FIG. 13 is a blockdiagram of a data processing system including a computer system 1300 inaccordance with an embodiment. The computer system can include orexecute a coherency filter component. The data processing system,computer system or computing device 1300 can be used to implement one ormore components configured to process data or signals depicted in FIGS.1-12 and 14-16. The computing system 1300 includes a bus 1305 or othercommunication component for communicating information and a processor1310 a-n or processing circuit coupled to the bus 1305 for processinginformation. The computing system 1300 can also include one or moreprocessors 1310 or processing circuits coupled to the bus for processinginformation. The computing system 1300 also includes main memory 1315,such as a random access memory (RAM) or other dynamic storage device,coupled to the bus 1305 for storing information, and instructions to beexecuted by the processor 1310. Main memory 1315 can also be used forstoring time gating function data, temporal windows, images, reports,executable code, temporary variables, or other intermediate informationduring execution of instructions by the processor 1310. The computingsystem 1300 may further include a read only memory (ROM) 1320 or otherstatic storage device coupled to the bus 1305 for storing staticinformation and instructions for the processor 1310. A storage device1325, such as a solid state device, magnetic disk or optical disk, iscoupled to the bus 1305 for persistently storing information andinstructions.

The computing system 1300 may be coupled via the bus 1305 to a display1335 or display device, such as a liquid crystal display, or activematrix display, for displaying information to a user. An input device1330, such as a keyboard including alphanumeric and other keys, may becoupled to the bus 1305 for communicating information and commandselections to the processor 1310. The input device 1330 can include atouch screen display 1335. The input device 1330 can also include acursor control, such as a mouse, a trackball, or cursor direction keys,for communicating direction information and command selections to theprocessor 1310 and for controlling cursor movement on the display 1335.

The processes, systems and methods described herein can be implementedby the computing system 1300 in response to the processor 1310 executingan arrangement of instructions contained in main memory 1315. Suchinstructions can be read into main memory 1315 from anothercomputer-readable medium, such as the storage device 1325. Execution ofthe arrangement of instructions contained in main memory 1315 causes thecomputing system 1300 to perform the illustrative processes describedherein. One or more processors in a multi-processing arrangement mayalso be employed to execute the instructions contained in main memory1315. In some embodiments, hard-wired circuitry may be used in place ofor in combination with software instructions to effect illustrativeimplementations. Thus, embodiments are not limited to any specificcombination of hardware circuitry and software.

Although an example computing system has been described in FIG. 13,embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in other types ofdigital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.

III. Controller for Exoskeleton

A controller can be provided to generate commands for an exoskeleton tocontrol the operation of the exoskeleton, for example, in real-time as auser is performing one or more activities based in part on real-timedata (e.g., sensor data) associated with the user performing the one ormore activities to augment and aid the user through the exoskeleton inperforming the activities. The controller can update or modify commandsindicating torque to be applied to a limb of the user through theexoskeleton based in part on feedback as the user is performing theactivities. In embodiments, the controller can generate commands tocorrect or provide a desired level of torque or force through theexoskeleton to aid the user in performing the activities at the corrector appropriate time, for example, using the real-time feedback.

A controller can be designed for a predetermined steady state gait for aperson (e.g., subject A walking at 3 mph on a treadmill). A softwareengineer can collect data and then use heuristics to produce a targettorque profile for controlling operation of an exoskeleton based on thecollected data. Exoskeleton controllers may be difficult to design basedon a variety of factors, including subtle or significant differencesbetween different users' ambulation profiles, the application of torqueor force affects different users' gait in unknown ways, the number ofconditions the controller may need to account for, the control oftransitions between different states, the lack of a single cost-functionthat can be optimized in real-time, and the lack of a clarity of whatsensors should be used to predict the target torque. Conditions that thecontroller may need to account for can include different types of people(e.g., age, size, ability, etc.), different types of gait (e.g.,walking, running, jumping, etc.), different terrains (e.g., pavement,grass, sand, ice, etc.), different speeds (e.g., slow, medium, fast,etc.), different target power levels (e.g., high augmentation,transparent, low, etc.). The transition between different states canhave O(2) occurrences. If the controller supports N discrete steadystate behaviors, then there can be approximately N² possibletransitions.

Exoskeleton controllers as described herein can convert real-time sensordata into motor commands. Exoskeleton controllers can be broken intothree levels: high level, mid-level, and low level. High levelcontrollers can include activity recognition (e.g., walking, running,sitting, etc.). Mid-level controllers can include development of atorque profile based on recognized activity (e.g., converting activityinto torque). Low level controllers can include execution of themid-level torque profile (e.g., motor commands, field oriented controlof brushless DC motors, current causing torque or speed, etc.).Functions can be developed that first recognize an activity and then useadditional algorithms that develop torque profiles. However, variousfactors including those enumerated above can make it technicallydifficult or challenging to determine the torque profile for aparticular activity is. Functions including a high level, mid-level, andlow level controller can be termed 3 L controllers because the algorithmhas 3 levels. Functions including a high level and low level controllercan be termed 2 L controllers because the algorithm has 2 levels. Thefunction can convert sensor data into motor commands. For example, thefunction can determine the torque and then execute (e.g., apply) thetorque.

3 L controllers can be developed by engineers for specific actions. Thetesting environment can be controlled to known conditions where thecontroller behaves correctly. Data can be collected while using the 3 Lcontrollers. Machine learning can be used to predict 3 L controllertorques during these conditions. The machine learning controllers mayhave interpolation and extrapolation capabilities that go beyond thecapabilities of the 3 L controllers. The machine learning controller maycorrectly predict the required torque between states that the 3 Lcontroller does not control for. The machine learning controller mayreduce the number of 3 L controllers that need to be written byengineers. The machine learning controller may learn how to interpolateand extrapolate the controller to untrained movements, including gaittransitions. This may reduce the number of hand-written controllers andmake transitions between states more seamless. However, engineers maystill need to create the 3 L controllers to train the machine learningengine. These controllers may be practically difficult and timeconsuming to create (e.g., one controller can take months to develop) sothat they generalize across people. For example, even if one knows auser will be walking upstairs, it may be difficult to write a controllerthat applies the correct torque when anyone goes up the stairs. The bulkof a control developer's work can be in developing the algorithms tocreate a target torque profile given a certain gait. Machine learningcan occur from heuristic torque controllers. The sensor input may notchange if the machine learning torque command is identical.

Systems and methods in accordance with this technical solution canreceive sensor data from one or more sensors monitoring a user, such asa user in motion. The sensor data can include data from position sensorsof a motion capture system (e.g., accelerometers, gyroscopes) coupledwith the user, as well as image data (e.g., video data) from one or moreimage capture devices (e.g., cameras, including three-dimensionalcameras). The sensor data from multiple sensors can be correlated basedon timestamps at which the sensor data is detected. The sensor data or arepresentation thereof can be presented to an operator, such as anexpert, using a user interface (e.g., a display that presents the sensordata). An indication of a torque profile can be received from the userinterface, such as responsive to the operator drawing the torque profileon the representation of the sensor data. The torque profile can includean indication of torque at a plurality of points in time correspondingwith the sensor data. The torque profile can include an indication ofrelationships between parameters such as torque, time, and angle. Amachine learning model can be trained using training data that includessensor data as input and torque profiles as output. For example, thesensor data can be provided as input to the machine learning model,which can be caused to generate a candidate output. The candidate outputcan be compared with the torque profile, and the machine learning modelcan be modified responsive to the comparison, such as by using anoptimization algorithm to reduce or minimize a difference between thecandidate output and the torque profile, such as by adjusting variousweights or biases associated with components of the machine learningmodel. As such, the machine learning model can be trained to determinetorque profiles using sensor data without requiring complex,predetermined heuristics to be applied to first detect the activity,then determine the torque profile based on the activity.

In some embodiments, users perform various activities (e.g., steadystate, transient, etc.), while wearing a collection of sensors. Theusers can be videotaped or part of a motion capture system. An expertcan replay the data (e.g., using video) and generate the correct (e.g.,ground truth) torque profiles post-hoc. This could be done with avisualization software that allows the expert to step through the trialand simultaneously see the required data. The expert can “draw” thetorque profiles to what the expert believes is optimal. The activitiesmay or may not be tagged. The expert can add context to the transitions.Machine learning can be used to learn these torque profiles with theconstraint of only using real-time sensor data (e.g., without usingfuture data). Users can wear the devices using the machine learningmodels and real-time optimization is used to alter the commands to reachthe target torque profiles. Users may have the ability to alter theirown profiles in real-time, further informing the models. The machinelearning engine can determine the level of torque that should be appliedto the exoskeleton at any given point in time.

In some embodiments, there is an additional step after the expert drawsa few torque profiles. Machine learning techniques as described hereincan be used to replace the expert. For example, an expert can producethe torque profile for 100 different steps. Machine learning can be usedin an unconstrained manner to learn the expert's techniques with accessto all the data (e.g., past data, future data, etc.) including thedevice data and any extra sensors (e.g., motion capture, video, forceplates, etc.). In this way, the machine learning engine can generatemuch more training data much faster for the real-time machine learningmodel that is being used on the device.

Benefits of the aforementioned embodiments can include the following.The control developer may not need to create 3 L controllers. It can bemuch easier and faster to create post-hoc torque profiles. The expert orcontrol developer can use their understanding of biomechanics and theiraccess to unconstrained data to generate torque profiles quickly. Theexpert can see transitions and determine how the transitions should bemanaged. The transitions do not need to be anticipated for thetransitions are simply observed.

An expert can initially label data that is collected during unpowereduse of an exoskeleton. Once the expert's torque profiles are used todevelop a controller, a user can try to use this controller. Theapplication of torque can affect the sensor readings. For example,compliance in the system will affect the ankle angle measurement. If thebiological ankle is held at 90 degrees, the ankle angle sensor may read90 degrees when unpowered, but once torque is applied, it may readsomething different. The issue can be that for a given gait, the sensorreadings can be different than what the expert was using. This may notaffect the expert's ability to repeat the labeling process, but it mayaffect the machine learning model.

In some embodiments, iterative training cycles (e.g., cycle 1, cycle 2,cycle 3, cycle 4, etc.) can be used to converge. Cycle 1 can includeunpowered data. Cycle 2 can include imperfect powered data. Cycle 3 caninclude improved powered data. Additional cycles can include improvedpowered data over the previous cycle. An iterative approach can be usedduring the gathering of sensor data.

In some embodiments, characterization and system identification (ID)techniques can be used to predict how the application of torque willaffect the sensor readings. System ID can be done to map exoskeletontorque to sensor changes, or artificial intelligence (AI) can be used tocreate this map. The AI can use this to model the “unpowered” sensorreadings when torque is being applied. For example, the machine learningengine can learn a torque trajectory based on unpowered sensor data(e.g., from an expert or machine learning engine trained by expert). Themachine learning engine can begin to apply torque. The machine learningengine can use a characterization model to convert sensors reading underload to unpowered sensor readings. The machine learning engine can usethe simulated unpowered sensor reading to calculate appropriate torque.

In some embodiments, a 2 L controller can be done without AI. In someembodiments, experts could also tag basic features (e.g., toe-off, heelstrike, etc.) to fine tune the lower level algorithms. In someembodiments, an expert can list what transitions are possible or likely.For example, if a user is running, the chances that the user is sittingin the next step are low. In some embodiments, the machine learningengine could be learning the parameters to a physiological model insteadof a wide open space. Forcing the machine learning engine to generate anunconstrained torque profile may be impractical. The machine learningengine can fit within a model (e.g., an impedance controller that canonly update at a low frequency). The model can be physiologicallyinspired, like a muscle model. The machine learning engine can fit themodel parameters and not generate the entire torque profile. In someembodiments, constraints can be placed on the system. An exampleconstraint can include preventing the machine learning engine fromswitching the torque from 0 (or no torque) to maximum torqueinstantaneously (e.g., within a predetermined amount of time such).

Referring to FIG. 14, depicted is a block diagram of one embodiment of asystem 1400 having an exoskeleton boot 100 for augmenting motion of auser 1402 during one or more activities 1412. The exoskeleton boot 100can be the same as or substantially similar to exoskeleton 100 describedherein with respect to FIG. 1 or any type of exoskeleton describedherein. The exoskeleton boot 100 can include one or more components tocouple with a lower limb of the user 1402. For example, the exoskeletonboot 100 can include a shin pad 125 to couple to a shin of the user 1402below a knee of the user 1402. The exoskeleton boot 100 can include oneor more housings 105. At least one of the housings 105 can couple to theshin pad 125 below the knee of the user 1402. The housings 105 canenclose or include a controller 1410 having a memory 1404 and one ormore processors 1406, for example, coupled to the memory 1404. Thehousings 105 can enclose or include, but not limited to, an electricmotor 330 that generates to torque about an axis of rotation of an anklejoint of the user 1402. The housings 105 can provide protection for thecontroller 1410 and electronic motor 330 from various environmentalelements or conditions (e.g., water, rain, snow, mud, dirt) of anenvironment the exoskeleton boot 100 is being used or worn. The housing105 can be formed to cover or encapsulate the electronic circuitry,sensors and/or motors, including the controller 1410 and electronicmotor 330.

The exoskeleton boot 100 can include a battery holder 170 coupled to theshin pad 125. The battery holder 170 can include or correspond to acavity, compartment, chamber or structure shaped and designed to hold abattery module 145, for example, in place during operation or use of theexoskeleton boot 100. The exoskeleton boot 100 can include an outputshaft 605 coupled to the electric motor 330. For example, the outputshaft 605 can extend through a bore 610 in a housing 105 of the one ormore housings 105 enclosing the electric motor 330 to couple to theelectric motor 330. The shin pad 125, housing 105, battery holder 170,output shaft 605 can be the same as or substantially similar to shin pad125, housing 105, battery holder 170 described herein with respect toFIG. 1 and the output shaft 605 described above with respect to FIG. 6.or any type of exoskeleton described herein.

The exoskeleton boot 100 an include a controller 1410. The controller1410 can be implemented using hardware or a combination of software andhardware. For example, each component of the controller 1410 can includelogical circuitry (e.g., a central processing unit or CPU) thatresponses to and processes instructions fetched from a memory unit(e.g., memory 1404). Each component of the controller 1410 can includeor use a microprocessor or a multi-core processor. A multi-coreprocessor can include two or more processing units (e.g., processor1406) on a single computing component. Each component of the controller1410 can be based on any of these processors, or any other processorcapable of operating as described herein. Each processor can utilizeinstruction level parallelism, thread level parallelism, differentlevels of cache, etc. For example, the controller 1410 can include atleast one logic device such as a computing device having at least oneprocessor 1406 to communicate, for example, with a user device, displaydevice 1335 and one or more exoskeleton boots 100. The components andelements of the controller 1410 can be separate components or a singlecomponent. The controller 1410 can include a memory component (e.g.,memory 1404) to store and retrieve sensor data 1420. The memory 1404 caninclude a random access memory (RAM) or other dynamic storage device,for storing information, and instructions to be executed by thecontroller 1410 and command modelling system 1416. The memory 1404 caninclude at least one read only memory (ROM) or other static storagedevice for storing static information and instructions for thecontroller 1410. The memory 1404 can include a solid state device,magnetic disk or optical disk, to persistently store information andinstructions. The controller 1410 can be the same as or substantiallysimilar to any controller or microcontroller described herein.

The controller 1410 can include or connect with a command modellingsystem 1416. The command modelling system 1416 can include, generateand/or execute a model 1424 to generate commands 1426. The commandmodelling system 1416 can be implemented using hardware or a combinationof software and hardware. The command modelling system 1416 can includelogical circuitry (e.g., a central processing unit or CPU) thatresponses to and processes instructions fetched from memory 1404. Thecommand modelling system 1416 can include a processor and/or communicatewith processor 1406 to receive instructions and execute instructions(e.g., train model 1424) received, for example, from controller 1410.

The model 1424 can include or execute a machine learning device 1414(e.g., machine learning engine) having one or more machine learningalgorithms. In embodiments, the model 1424 can be trained to predicttorque values 1428 and torque profiles 1422 and generate one or morecommands 1426 corresponding to the torque values 1428 and torqueprofiles 1422. The machine learning device 1414 can identify patterns orsimilarities between different data points of the received input (e.g.,sensor data 1420) and map the inputs to outputs that correspond to theidentified patterns (e.g., ankle angle data, torque used to transitionbetween walking and running in previous activities). The model 1424 cangenerate the commands 1426 based in part on the identified patterns inthe received input data. The machine learning device 1414 can beimplemented using hardware or a combination of software and hardware. Inembodiments, the machine learning device 1414 can include circuitryconfigured to execute one or more machine learning algorithms.

The exoskeleton boot 100 can couple with or connect to (e.g., wirelessconnection) to a display 1335 (e.g., display device). The display 1335can provide, for example, information to the user 1402 including but notlimited to, torque profiles 1422, historical video data 1450, historicalmotion capture data 1450 and/or data associated with a user 1402performing one or more activities 1412 wearing the exoskeleton boot 100.The display 1335 can provide or display one or more visual indications1440. The visual indication 1440 can include a video of the user 1402performing an activity 1412, an image of the user 1402 performing anactivity 1412, a marker, menu, window or selectable content itemprovided through the display 1335. The visual indication 1440 caninclude a menu or listing of torque profiles 1422 available forselection through the display 1335 or user interface 1330 portion of thedisplay 1335 (e.g., touch screen, selectable content items). The display1335 can be the same as or substantially similar to the display 1335described above with respect to FIG. 13.

In embodiments, a user interface 1330 (e.g., input device) can couplewith or connect to the display 1335 to, for example, enable a user 1402to interact with content provided through the display 1335. The userinterface 1330 can include enable interaction with one or more visualindications 1440 provided through the display 1335 and responsive to aninteraction (e.g., select, click-on, touch, hover), the user interface1330 can generate an indication 1442 identifying a user input and/orselection of at least one content item (e.g., visual indication 1440).The user interface 1330 can couple to or connect with the exoskeletonboot 100 to provide the indication 1442. In some embodiments, thedisplay 1335 can receive the indication 1442 from the user interface1330 and transmit or provide the indication 1442 to the exoskeleton boot100. The user interface 1330 can be the same as or substantially similarto the input device 1330 described above with respect to FIG. 13.

The controller 1410 can store and maintain data, including sensor data1420, based in part on time intervals 1430 corresponding to a timeperiod when one or more activities 1412 were performed. Time intervals1430 can include or correspond to a time period or range of time havingan initial time and an end time. The number of time intervals 1430 canvary (e.g., first time interval 1430, second time interval 1430) and bebased at least in part on a number of activities 1412 tracked, a numberof users 1402, and/or an amount of sensor data 1420.

The sensor data 1420 can include, but is not limited to, motion data,force data, torque data, temperature data, speed, gait transitions,angle measurements (e.g., of different joints of the user 1402). Thesensor data 1420 can include data corresponding to steady stateactivities 1412 or transient activities 1412. The sensor data 1420 caninclude any form of data associated with, corresponding to or generatedin response one or more activities 1412 performed or executed by theuser 1402 wearing the exoskeleton boot 100. For example, the sensor data1420 can include data associated with a movement or motion performed orexecuted by the user 1402 and/or any type of use of one or more musclesof the user 1402, for example, that may not involve motion (e.g.,holding a position, standing) while wearing the exoskeleton boot 100.The sensor data 1420 can include ankle joint data, inertial measurementunit data, and/or battery data.

In embodiments, the sensor data 1420 can include historical data 1450.The historical data 1450 can include historical sensor data 1450,historical video data 1450 and historical motion capture data 1450. Thehistorical sensor data 1450 can include previous sensor data 1420associated with the user 1402 performing one or more activities 1412 orsensor data 1420 from one or more other, different users 1402 performingone or more activities 1412. The historical video data 1450 can includeone or more videos, images or stream of images of the user 1402 and/orone or more other, different users 1402 performing one or moreactivities 1412. The historical motion capture data 1450 can include oneor more recordings or images of the user 1402 and/or one or more other,different users 1402 performing one or more activities 1412.

The historical motion capture data 1450 can include or correspond todata collected via the exoskeleton boot 100 in a plurality of states,for example, an unpowered state, a partially powered state, and a fullypowered state. The historical motion capture data 1450 can includeinertial measurement unit data, goniometer data, infrared reflectordata, force plate data, electromyography (EMG) data, and heartrate data.The historical data 1450 can be received from a plurality of differentsystems (e.g., plurality of sensors, plurality of exoskeleton boots,plurality of user devices, plurality of controllers) and the controller1410 can perform one or more of the following, averaging, filtering,aggregating and/or merging to process the historical data 1450 andprovide to the model 1424. For example, the controller 1410 can averagethe historical data 1450 to identify patterns, trends or similaritiesacross different data points. The controller 1410 can filter thehistorical data 1450 to identify patterns, trends or similarities acrossdifferent data points. The controller 1410 can aggregate or merge thehistorical data 1450 to identify patterns, trends or similarities acrossdifferent data points. In embodiments, the controller 1410 can generatea data set using the historical data 1450 to provide to the model 1424for training the model 1424.

The commands 1426 can include an instruction, task or function generatedby the model 1424 and provided to an exoskeleton boot 100 to instructthe exoskeleton boot 100 a level or amount of torque, force, velocity ora combination of torque, force and velocity (e.g., impedance) togenerate to aid a user wearing the respective exoskeleton boot 100 inperforming an activity 1412. In embodiments, the commands 1426 caninclude a data structure indicating a desired, requested or targettorque, force and/or velocity level. The commands 1426 can include orcorrespond to a torque profile 1422 that includes one or more torquevalues 1428 (e.g., or force values, velocity values) for the exoskeletonboot 100 to apply to a lower limb of the user 1402 to augment or aid theuser 1402 in performing an activity 1412.

Referring now to FIG. 15, depicted is a flow diagram of one embodimentof a method 1500 for method of augmenting motion via a battery-poweredactive exoskeleton boot in accordance with an illustrative embodiment.In brief overview, the method 1500 can include one or more of: providinga shin pad of an exoskeleton boot (1502), providing a housing (1504),providing a battery holder (1506), providing an output shaft (1508),performing an activity (1510), receiving sensor data (1512), identifyingone or more torque profiles (1514), providing a visual indication(1516), receiving an indication (1518), training the model (1520),determining one or more commands (1522), transmitting one or morecommands (1524), and performing a subsequent activity (1526). Thefunctionalities of the method 1500 may be implemented using, orperformed by, the components detailed herein in connection with FIGS.1-14 and 16.

Referring now to operation (1502), and in some embodiments, a shin pad125 can be provided, for example, of an exoskeleton boot 100 forcoupling to a shin of a user 1402 below a knee of the user 1402. Theshin pad 125 can be a component or portion of the exoskeleton boot 100.The shin pad 125 can be coupled to (e.g., connected to, attached to,directly connected to) to the exoskeleton boot 100. The shin pad 125 cancouple with or contract the shin of the user 1402, for example, to aidin connecting or securing the exoskeleton boot 100 to a lower limb ofthe user 1402. The shin pad 125 can be positioned, when the user 1402 iswearing the exoskeleton boot 100, to provide support and/or comfort tothe respective lower limb that the exoskeleton boot 100 is coupled.

Referring now to operation (1504), and in some embodiments, one or morehousings 105 can be provided. The exoskeleton boot 100 can include oneor more housings 105 to hold, enclose or contain, but not limited to,electronic circuitry, sensors and/or motors of the exoskeleton boot 100.For example, the housings 105 can enclose or include a controller 1410having a memory 1404 and one or more processors 1406, for example,coupled to the memory 1404. The housings 105 can enclose or include, butnot limited to, an electric motor 330 that generates to torque about anaxis of rotation of an ankle joint of the user 1402. The housings 105can provide protection for the controller 1410 and electronic motor 330from various environmental elements or conditions (e.g., water, rain,snow, mud, dirt) of an environment the exoskeleton boot 100 is beingused or worn. The housing 105 can be formed to cover or encapsulate theelectronic circuitry, sensors and/or motors, including the controller1410 and electronic motor 330. The positioning of the housings 105 onthe exoskeleton boot 110 can vary, based at least in part on a type ofexoskeleton 100 and one or more other components (e.g., shin pad 125,encoders 155, 160) of the exoskeleton 100. In embodiments, at least oneof the one or more housings 105 can be coupled to (e.g., connected to)the shin pad 125 below the knee of the user 1402.

Referring now to operation (1506), and in some embodiments, a batteryholder 170 can be provided, for example, coupled to the shin pad 125.The battery holder 170 can be configured to receive, connect to or holda battery module 145. The battery holder 170 can include or correspondto a cavity, compartment, chamber or structure shaped and designed tohold the battery module 145, for example, in place during operation oruse of the exoskeleton boot 100. In embodiments, the battery holder 170can secure or hold the battery module 145 motionless (or limit movementof battery module 145) during operation or use of the exoskeleton boot100. In some embodiments, the battery holder 170 can enclose the batterymodule 145 and include material to provide protection for the batterymodule 145 from various environmental elements or conditions of anenvironment the exoskeleton boot 100 is being used or worn. Thepositioning of the battery holder 170 on the exoskeleton boot 110 canvary, based at least in part on a type of exoskeleton 100 and one ormore other components (e.g., shin pad 125, encoders 155, 160) of theexoskeleton 100. In embodiments, the battery holder 170 can couple withor connect to the shin pad 125 of the exoskeleton boot 100 and below theknee of the user 1402.

Referring now to operation (1508), and in some embodiments, an outputshaft 605 can be provided, for example, coupled to the electric motor330 and extending through a bore 610 in a housing 105 of the one or morehousings 105 enclosing the electric motor 330. The output shaft 605 canconnect to (e.g., directly connect to) the electric motor 330. Inembodiments, the output shaft 605 can extend through a bore 610 in ahousing 105 of the one or more housings 105 enclosing the electric motor330 to couple with the electric motor 330.

Referring now to operation (1510), and in some embodiments, an activity1412 can be performed using the exoskeleton boot 100. The exoskeletonboot 100 can augment or aid the user 1402 in performing one or moreactivities 1412. In embodiments, the exoskeleton boot 100 can provideforce, torque and/or power to the lower limb of the user 1402 theexoskeleton boot 100 is coupled to with to augment the movement of theuser 1402 during the activity 1412. The activity 1412 can include steadystate activities or transient activities. The activity 1412 can vary andcan include any type of movement or motion performed or executed by theuser 1402 and/or any type of use of one or more muscles of the user1402, for example, that may not involve motion (e.g., holding aposition, standing). For example, the activity 1412 (e.g., physicalactivity 1412) can include, but is not limited to, walking, running,standing, standing up, ascend or descend a surface (e.g., stairs),jogging, springing, jumping (e.g., single leg or both legs) squat,crouch, kneel or kick. In embodiments, the exoskeleton boot 100 cantransfer energy to the lower limb of the user 1402 to augment themovement of the user 1402 during the activity 1412. The exoskeleton boot100 can reduce a difficulty of performing the respective activity 1412or multiple activities 1412 by reducing the energy or effort the user1402 exerts to perform the respective activity 1412.

In some embodiments, the activities 1412 can include an initial activity1412 or test activity 1412 performed under determined or specificconditions to generate and obtain sensor data 1420. For example, theactivities 1412 an include specific actions (e.g., walk, run, jump) totest a performance of the user 1402 using the exoskeleton boot 100 andgenerate initial or baseline sensor data 1420. The activities 1412 canbe performed in specific conditions or under test conditions, such asbut not limited to, indoors, outdoors, or jumping to specific heights,where the conditions are known and can be factored with or aggregatedwith the associated sensor data 1420 to generate baseline sensor data1420 for the user 1402.

In embodiments, different users can ambulate or move differently and theapplication of force or torque can affect gait in different ways. Theuser 1402 can perform a variety of different activities 1412, steadystate and transient, while wearing a plurality of sensors and one ormore exoskeleton boots 100. In embodiments, the user 1402 can bevideotaped or recorded being in a motion capture system to generatevideo data and/or motion capture data associated with the activities1412. The activities 1412 can include test conditions that apply torqueor force to the user through the exoskeleton boot 100 to determine andlearn how the specific user 1402 ambulates, moves and how a gait of theuser is affected using the exoskeleton boot 100. In some embodiments,the test activities 1412 can include different power levels of theexoskeleton boots 100. For example, an ankle angle measurement mayprovide a first value when the exoskeleton boot 100 is unpowered and asecond, different value when torque is applied via a powered exoskeletonboot 100. Thus, the user 1402 can perform activities 1412 and bemeasured in different positions (e.g., sitting, standing) when theexoskeleton boot 100 is unpowered and powered through different trainingcycles to better learn movement patterns of the user 1402 (e.g., cycle1: unpowered data, cycle 2: imperfect powered data, cycle 3: betterpowered data). In embodiments, the test activities 1412 can include, butare not limited to, different types of gait (e.g., walking, running,jumping), different terrains (e.g., pavement, grass, sand, ice),different speeds (e.g., slow, medium, fast), and different power levels(e.g., high augmentation, transparent, low).

In some embodiments, the activity 1412 can include activities ormovements performed in a simulator environment or using a simulator andthe user can be connected to equipment operating as or mimicking theexoskeleton boot 100 (or exoskeleton device). The simulator environmentcan be used to test different toque profiles 1422, torque values 1428and/or commands 1426 prior to providing the values to an exoskeletonboot 100. For example, a user can be connected to equipment thatincludes, but is not limited to, cables (e.g., Bowden cables), braces,motors, controllers and/or other types of devices or equipment capableof providing torque to one or more joints of the user. The controller1410 can be connected to the simulator environment and the equipment ofthe simulator environment to generate and provide one or more torqueprofiles 1422 to one or more joints of a user through the equipment ofthe simulator environment. The controller 1410 can generate one or morecommands 1426 indicating a torque profile 1422 and/or one or more torquevalues 1428 associated with a torque profile 1422 to provide a targetlevel of torque to the joints of the user. In embodiments, the equipmentof the simulator can provide force, torque and/or power to the lowerlimb of the user 1402 to augment the movement of the user 1402 duringthe activity 1412. The activity 1412 can include steady state activitiesor transient activities. The activity 1412 can vary and can include anytype of movement or motion performed or executed by the user 1402 and/orany type of use of one or more muscles of the user 1402, for example,that may not involve motion (e.g., holding a position, standing).

Referring now to operation (1512), and in some embodiments, sensor data1420 can be received by the controller 1410. The sensor data 1420 can beassociated with or correspond to an activity 1412 of the exoskeletonboot 100 during a first time interval 1430. The sensor data 1420 can bereceived from one or more sensors coupled to (e.g., wirelessly coupled,directed connected) or that are components of the exoskeleton boot 100.The sensor data 1420 can include, but is not limited to, motion data,force data, torque data, temperature data, speed, gait transitions,angle measurements (e.g., of different joints of the user 1402).

The sensor data 1420 can include data corresponding to steady stateactivities 1412 or transient activities 1412. The sensor data 1420 caninclude any form of data associated with, corresponding to or generatedin response one or more activities 1412 performed or executed by theuser 1402 wearing the exoskeleton boot 100. For example, the sensor data1420 can include data associated with a movement or motion performed orexecuted by the user 1402 and/or any type of use of one or more musclesof the user 1402, for example, that may not involve motion (e.g.,holding a position, standing) while wearing the exoskeleton boot 100. Inembodiments, the sensor data 1420 can include or correspond to dataretrieved from or obtained from a video or recording of the activity1412 performed by the user 1402. The controller 1410 can receive a videoor recording of the user 1402 performing the activity 1412 and determineor obtain sensor data 1420 from the video data or motion capture data.

The historical data 1450 can include sensor data 1420 from a number ofdifferent types of people or users, for example, people of varying age,size, and/or ability. In some embodiments, the controller 1410 canreceive or obtain historical video data 1450 and/or historical motioncapture data 1450 from one or more users 1402 (e.g., same body profile,same activity 1412 performed, same genetic traits) similar to therespective user 1402 using the exoskeleton boot 100 to compare and/ordetermine sensor data 1420 for the user 1402. The sensor data 1420 fromthe one or more similar users 1402 can be used to determine an averageor identify anomalies in the sensor data 1420 obtained from the user1402 performing the activity 1412 while wearing the exoskeleton boot100. For example, a command modelling system 1416 can receive historicalvideo data associated with one or more users 1402 performing one or morephysical activities 1412. In embodiments, the command modelling system1416 can receive historical motion capture data that includes historicalsensor data.

Referring now to operation (1514), and in some embodiments, one or moretorque profiles 1422 can be identified. The controller 1410 candetermine torque profiles 1422 corresponding to or based in part on theactivities 1412 performed by the user wearing the exoskeleton boot 100and the sensor data 1420 associated with the activities 1412. Inembodiments, the controller 1410 or command modelling system 1416 candetermine the one or more torque profiles 1422 corresponding to the oneor more physical activities 1412 based on the historical video data. Thetorque profile 1422 can include or represent a level of torque or torquevalue 1428 for the exoskeleton boot 100 to apply or provide to the lowerlimb of the user during an activity 1412 to augment or aid the user 1402in performing the activity 1412. In embodiments, the torque profile 1422can include or represent a level of force for the exoskeleton boot 100to apply or provide to the lower limb of the user during an activity1412 to augment or aid the user 1402 in performing the activity 1412.The torque profile 1422 can include a series of torque values 1428 (orforce values) for the exoskeleton boot 100 to apply or provide to thelower limb of the user during an activity 1412 to augment or aid theuser 1402 at different points or stages in the respective activity 1412in performing and completing the activity 1412. For example, theactivity 1412, such as standing up and jumping, can include a series ofmovements and each movement (e.g., plant foot, flex ankle, beginstanding up, straighten leg, jump) can include a different toque value1428 (e.g., standing up, walking, jumping) that the exoskeleton appliesto the lower limb of the user to augment the user 1402 in performing therespective movement and thus, completing the activity 1412.

The controller 1410 can determine the torque values 1428 to generate oneor more torque profiles 1422 based in part on the received sensor data1420 and/or historical data (e.g., historical video data, historicalmotion capture data) that represents or includes data identifying howmuch aid the user 1402 may have needed in performing similar activities1412 or movements previously. In embodiments, the torque profile 1422can include predictions or predicted torque values 1428 that arepredicted using the sensor data 1420 from the user 1402 performing oneor more activities 1412 (e.g., same activities, similar activities)and/or one or more other users 1402 performing one or more activities1412.

The controller 1410 can execute a machine learning device 1414 toreceive the sensor data 1420 and predict and generate the torque values1428 and torque profiles 1422. The machine learning device 1414 canpredict a needed or desired torque value 1428 to perform one or moreactivities 1412. For example, the sensor data 1420 can include dataassociated with the user 1402 or other users 1402 walking, running,flexing an ankle, flexing a knee or jumping. The sensor data 1420 caninclude conditions (e.g., environmental, user specific) that theactivities 1412 were performed under such as, but not limited to,indoors, outside, in the rain, male user, female user, type of gait. Thesensor data 1420 can include or correspond to historical video data ofthe user 1402 performing one or more activities 1412 and/or historicalmotion capture data of the user 1402 performing one or more activities1412.

The machine learning device 1414 can receive the sensor data 1420including the type of activities 1412 and conditions as inputs and, forexample using a machine learning algorithm, generates outputs aspredicted torque values 1428 for the user 1402 to augment the user 1402performing one or more activities 1412 in the future under the same ordifferent conditions. In some embodiments, the inputs can include userprovided inputs. For example, an administrator or user can provide datato modify or aggregate with the sensor data 1420. The user providedinputs can include data associated with the user 1402 performing one ormore activities 1412, user physical parameters, user measurements, andbiometrics. The machine learning device 1414 can predict torque values1428 to augment the user 1402 transitioning between different states(e.g., active to rest, steady state to transient) and transitioningbetween different gaits (e.g., walking to running).

Referring now to operation (1516), and in some embodiments, a visualindication 1440 can be provided, for example, through a display 1335. Inembodiments, a command modelling system 1416 can provide the visualindication 1440 through a display 1335, for example, a display device(e.g., computing device, mobile device) of a user device or of theexoskeleton boot 100. In embodiments, the command modelling system 1416can provide for display, via a display device 1335 communicativelycoupled to the command modelling system 1416, the visual indication 1440of the historical motion capture data. The command modelling system 1416can provide for display, via a display device 1335 communicativelycoupled to the command modelling system 1416, a visual indication 1440of the historical motion capture data.

The visual indication 1440 can include a video of the user 1402performing an activity 1412, an image of the user 1402 performing anactivity 1412, a marker, menu, window or selectable content itemprovided through the display 1335. The visual indication 1440 caninclude a menu or listing of torque profiles 1422 available forselection through the display 1335 or user interface 1330 portion of thedisplay 1335 (e.g., touch screen, selectable content items). The visualindication 1440 can be used to provide feedback to a user of the display1335 and/or allow the user of the display 1335 to provide feedback tothe controller 1410 and/or exoskeleton boot 100, such as but not limitedto, a selection of at least one torque profile 1422. The feedback can beused to generate one or more torque profiles 1422 or modify one or moretorque profiles 1422. The visual indication 1440 can generate anindication 1442 identifying input (e.g., a selection) by a user of thedisplay 1335 and corresponding to feedback from the user. For example,responsive to an interaction (e.g., click on, touch, hover, selection),the visual indication 1440 can generate and transmit an indication 1442identifying input provided by a user of the display 1335. In someembodiments, the indications 1442 can include user provided inputs. Forexample, an administrator or user can provide data to modify oraggregate with the sensor data 1420. The user provided inputs caninclude data associated with the user 1402 performing one or moreactivities 1412, user physical parameters, user measurements, andbiometrics (e.g., heartrate, EMG data). The machine learning device 1414can predict torque values 1428 to augment the user 1402 transitioningbetween different states (e.g., active to rest, steady state totransient) and transitioning between different gaits (e.g., walking torunning).

Referring now to operation (1518), and in some embodiments, anindication 1442 can be received, for example, through an input device1330 (e.g., user interface) coupled to the command modeling system. Theindication 1442 can include or correspond to an interaction with thevisual indication 1440 provided through the display 1335. Inembodiments, the indication 1442 can include a selection of at least onetorque profile 1422. In some embodiments, the indication 1442 caninclude data associated with one or more activities 1412 and/orassociated with one or more users 1402. The command modelling system1416 can receive, via a user interface 1330, an indication 1442 of atorque profile 1422 corresponding to the visual indication 1440 of thehistorical motion capture data. The command modelling system 1416 canreceive, via a user interface 1330, an indication 1442 of a type ofphysical activity 1412 corresponding to the visual indication 1440 ofthe historical motion capture data. In embodiments, the controller 1410can receive, via the user interface 1330, input from the user prior to asecond time interval 1430. The indication 1442 and/or input can be usedby the controller to modify the sensor data 1420 or can be aggregatedwith the sensor data 1420 to modify or update one or more torqueprofiles 1422.

In some embodiments, user input can be received or the indication 1442can include user input. The controller 1410 can receive via the userinterface user input from the user of the exoskeleton boot 100. Thecontroller 1410 can provide or connect to an application executing on aclient device or the exoskeleton boot 100 and provided through the userinterface 1330. In embodiments, the application can include an interface1330 to provide or modify sensor data 1420 and/or historical data 1450.In one embodiment, the application can include a torque tool to entertorque values and/or modify torque values 1428 including historicaltorque values 1428 for the user and stored or maintained in a memory1404 of the controller 1410. The user input can include, but is notlimited to, a modification or change to one or more sensor data valuesand/or historical data values. The user input can include a rating ofhow a previous activity 1412 felt to the user (e.g., last step feltgood, last step felt off), a user rating (e.g., a rating score, 0-10rating), a rating of how the exoskeleton boot 100 performed during aprevious activity 1412, and/or a value indicating a rate of perceivedexhaustion (RPE). In some embodiments, the application can provide orillustrate a graph of a torque profile 1422 having multiple data pointswith each data pint correspond to a relationship between at least onetorque value 1428 and at least one joint angle. The data points caninclude selectable or interactive content and the user can interact with(e.g., drag, touch, select) the different data points to modify torquevalues 1428 and/or the torque profile 1422 (e.g., in real-time) andadjust how the exoskeleton boot 100 is performing and/or feels to therespective user during an activity 1412. The controller 1410 can receivethe new or modified values and update a current or active torque value1422 and/or torque profile 1422 provided to the user, for example, tomodify a current or active torque provided to the user through theexoskeleton boot 100 in real-time. In embodiments, the controller 1410can receive from the application the new or modified values and updateat least one sensor data 1420 and/or historical data 1450 associatedwith the user. In some embodiments, the controller 1410 can use themodified sensor data 1420 and/or modified historical data 1450 to modifya torque profile 1422 for the user or generate a new torque profile 1422for the user.

Referring now to operation (1520), and in some embodiments, a model 1424can be trained. The controller 1410, for example through the commandmodelling system 1416, can generate and train the model 1424 byproviding the received sensor data 1420, historical data 1450, one ormore indications 1442, one or more torque profiles 1422 and/or otherforms of input as inputs to the model 1424 and execute the model 1424.In embodiments, the received sensor data 1420, historical data 1450, oneor more indications 1442, one or more torque profiles 1422 and/or otherforms of input as inputs can include or correspond to training dataprovided to the model 1424 and machine learning device 1414 to train themodel 1424 to predict outputs, here commands 1426 to instruct theexoskeleton boot 100. The model 1424 can include the machine learningdevice 1414 (e.g., machine learning engine) and a machine learningalgorithm such that as more and more inputs are received and provided tothe model 1424, the model 1424 can be trained to predict torque values1428 and torque profiles 1422 and generate one or more commands 1426corresponding to the torque values 1428 and torque profiles 1422. Themachine learning device 1414 can identify patterns or similaritiesbetween different data points of the received input and map the inputsto outputs that correspond to the identified patterns (e.g., ankle angledata, torque used to transition between walking and running in previousactivities). The model 1424 can generate the commands 1426 based in parton the identified patterns in the received input data.

In embodiments, the torque profiles 1422 can be used as inputs into themodel 1424 and to train the model 1424 to generate outputs correspondingto the commands 1426. The commands 1426 can include instructionsprovided to one or more components of the exoskeleton boot 100 togenerate a torque profile 1422 or a torque value 1428 of a series oftorque values 1428 forming a torque profile 1422. For example, thecommand modelling system 1416 can train, using the machine learningtechnique (e.g., machine learning device 1414) and based on the one ormore torque profiles 1422, the model 1424 to cause the model 1424 tooutput the one or more commands 1426 responsive to the sensor data 1420.The command modelling system 1416 can train, using the machine learningtechnique and based on the indication 1442 of the torque profile 1422received via the user interface 1330, the model 1424 to cause the model1424 to output the one or more commands 1426 responsive to the sensordata 1420. The command modelling system 1416 can train, using themachine learning technique and based on the indication 1442 of the typeof physical activity 1412 received via the user interface 1330, themodel 1424 to cause the model 1424 to output the one or more commands1426 responsive to the sensor data.

Referring now to operation (1522), and in some embodiments, one or morecommands 1426 can be determined. The controller 1410 can determine,based on the sensor data 1420 input into the model 1424 trained via amachine learning technique based on historical motion capture data 1420associated with one or more users 1402 performing one or more physicalactivities 1412, one or more commands 1426 for a second time interval1430 subsequent to the first time interval 1430. The controller 230 canobtain or receive the commands 1426 generated by the model 1424 for asubsequent activity 1412 to be performed by the user 1402 during thesecond time interval 1430. In embodiments, the controller 1410 canselect one or more commands 1426 from a plurality of commands 1426generated by the model 1424 based in part on an identified activity 1412to be performed by the user 1402 wearing the exoskeleton boot 100 duringthe second time interval 1430. The commands 1426 can include orcorrespond to one or more torque profiles 1422 to be provided to theexoskeleton boot 100 that include torque values 1428 for the exoskeletonboot 100 to apply to a lower limb of the user 1402 to augment or aid theuser 1402 in performing the subsequent or next activity 1412. Thecommands 1426 can include or correspond to instructions to control atorque, force, velocity or any combination of torque, force and velocity(e.g., impedance) applied to a lower limb of the user 1402 via theexoskeleton boot 100. The commands 1426 can include or correspond toinstructions to set a target level of torque, force, velocity or anycombination of torque, force and velocity (e.g., impedance) to beapplied to a lower limb of the user 1402 via the exoskeleton boot 100.The controller 1410 can determine the one or more commands 1426 for thesecond time interval 1430 to match a torque profile 1422 selected basedon the sensor data 1420 via the model 1424. In embodiments, thecontroller 1410 can generate, via the model 1424, the one or morecommands 1426 based on the input (e.g., indications 1442, user input)and the sensor data 1420.

Referring now to operation (1524), and in some embodiments, the one ormore commands 1426 can be transmitted. For example, the controller 1410can transmit the one or more commands 1426 generated based on the model1424 to the electric motor 330 to cause the electric motor 330 togenerate torque about the axis of the rotation of the ankle joint of theuser 1402 in the second time interval 1430. The electric motor 330 cangenerate torque corresponding to a torque profile 1422 and/or torquevalues 1428 identified in the one or more commands 1426 to cause theexoskeleton boot 100 to apply a force to a lower limb of the user 1402to augment or aid the user 1402 in performing the subsequent or nextactivity 1412.

Referring now to operation (1526), and in some embodiments, a subsequentactivity 1412 can be performed using the exoskeleton boot 100. Inembodiments, the exoskeleton boot 100 can provide force, torque and/orpower to the lower limb of the user 1402 the exoskeleton boot 100 iscoupled to with to augment the movement of the user 1402 during theactivity 1412 using the one or more commands 1426. In embodiments, thesubsequent activity 1412 can include a new activity 1412 or acontinuation of the first activity 1412 (e.g., second portion of initialactivity). The exoskeleton boot 100 can transfer energy to the lowerlimb of the user 1402, based on the one or more commands 1426 and torqueprofiles 1422 generated for the user 1402, to augment the movement ofthe user 1402 during the activity 1412. The exoskeleton boot 100 canreduce a difficulty of performing the respective activity 1412 ormultiple activities 1412 by reducing the energy or effort the user 1402exerts to perform the respective activity 1412. In some embodiments, themethod 1500 can return to operation (1512) to monitor for or wait forsubsequent sensor data 1420 associated with the subsequent activity1412. The controller 1410 can continue to monitor one or more activities1412 performed by the user 1402 wearing the exoskeleton boot 100 andobtain sensor data 1420 associated with the one or more activities 1412to generate more accurate commands 1426 and torque profiles 1422 for theuser 1402. For example, as the user performs additional activities 1412using the exoskeleton boot 100, the controller 1410 can provide sensordata 1420 associated with the additional activities 1412 to the model1424 to further train and refine the predictions and commands 1426generated using the model 1424 to provide a more customized userexperience for the respective user 1402 using the exoskeleton boot 100.

FIG. 16 is a block diagram of a system 1600 for training a model togenerate one or more commands in accordance with an illustrativeembodiment. In embodiments, the model 1424 can be trained usingdifferent data points (e.g., inputs) to predict and determine commands1426 to control, for example, operation and use of an exoskeleton boot100. The command modelling system 1416 of the controller 1410 canreceive the inputs and provide the inputs to the model 1424 to train themodel 1424 for one or more users 1402 of the exoskeleton device 100. Themodel 1424 can include a machine learning device 1414 to execute one ormore machine learning algorithms 1414 and/or artificial intelligence(AI) engines to turn the received inputs into a model and one or morepredictions for generating commands 1426.

The inputs can include but is not limited to, sensor data 1420,historical data 1450, indications 1442 and one or more torque profiles1422. The inputs can include sensor data 1420 associated with aplurality of users 1402 of varying ages, sizes and ability levels orusers 1402 in a similar age range, size range and/ability range as acurrent user 1402 of the exoskeleton boot 100. The inputs can includesensor data 1420 associated with a plurality of different types ofactivities, states (e.g., transient state, steady state) and/or powerlevels (e.g., unpowered, low power level, full power level) to learn andtrain the model 1424 across a variety of different movement patterns.

The command modelling system 1416 can provide one or more of the sensordata 1420, historical data 1450, indications 1442 and one or more torqueprofiles 1422 to execute and train the model 1424 at a time. In someembodiments, the command modelling system 1416 can continually provideone or more of the sensor data 1420, historical data 1450, indications1442 and one or more torque profiles 1422 to execute and train the model1424, for example, during a series of activities 1412 to update themodel 1424 and generate new subsequent commands 1426 as a user 1402transitions between the different activities 1412 in the series ofactivities 1412.

The sensor data 1420 can include real-time sensor data, for example,received as the user 1402 is performing an activity 1412 to enable themodel 1424 to be trained using real-time data and generate commands 1426using the real-time sensor data 1420. In embodiments, the users 1402 canwear the exoskeleton boots 100 and the controller 1410, through themodel 1424, ca provide real-time optimization to alter commands 1426 orgenerate new commands 1426 to reach a desired torque profile 1422. Insome embodiments, the user 1402 can provide real-time feedback to thecontroller 1410 and model 1424, for example, through selection of atorque profile 1422 (e.g., indication 1442) via a user interface 1330and alter the users own respective torque profile 1422 in real-time.

The command modelling system 1416 can receive historical data 1450 fromone or more users 1402 to provide a larger data set to train the model1424. For example, the command modelling system 1416 can providehistorical sensor data 1450 from different users 1402 to provide avariety of different data points that include information on variousconditions (e.g., environmental) and different type of users 1402 andgenerate an increased level of training data to train the model 1424initially prior a respective user 1402 generating a determined amount ofsensor data 1420 on their own.

The model 1424 can process the received inputs using the machinelearning device 1414 to apply one or more machine learning algorithmsand/or AI techniques to the received inputs and generate commands 1426for instructing and controlling the exoskeleton boot 100. For example,the model 1424 can be trained to predict torque values 1428 and torqueprofiles 1422 and generate one or more commands 1426 corresponding tothe torque values 1428 and torque profiles 1422. The machine learningdevice 1414 can identify patterns or similarities between different datapoints of the received input. The machine learning device 1414 can trainthe model 1424 to predict how the application of a particular level oftorque, force and/or velocity can impact the movement, gait and/orperformance of the user 1402 performing one or more activities 1412. Insome embodiments, the machine learning device 1414 can, for exampleusing AI, map or determine relationships between changes in sensor data1420 (e.g., changes in sensor readings) responsive to different levelsof torque, force and/or velocity provided to a lower limb of a user 1402through the exoskeleton boot 100 to predict how the user 1402 may reactto a determined levels of torque, force and/or velocity in one or morecurrent activities 1412 or future activities 1412. For example, themachine learning device 1414 can learn or identify patterns of a torquetrajectory based in part on provided sensor data 1420 (e.g., powereddata, unpowered data). The model 1424 can generate commands 1426 toapply torque through at least one exoskeleton boot 100 to a lower limbof the user 1402. The model 1424 can receive subsequent or follow-upsensor data 1420 associated with the user 1402 performing activities1412 using the exoskeleton boot 100 using the commands 1426. The machinelearning device 1414 can characterize the subsequent sensor data 1420 todetermine, for example, if a current level of torque is sufficient or ifa previously applied torque met the respective user's 1402 needs toperform the activity 1412. The machine learning device 1414 can use thecharacterization to further train and update the model 1424, forexample, for one or more subsequent activities 1412 performed by theuser 1402.

The commands 1426 can include instructions provided to one or morecomponents of the exoskeleton boot 100 to generate a torque profile 1422or a torque value 1428 of a series of torque values 1428 forming atorque profile 1422. The controller 1410 can determine, based on thesensor data 1420 input into the model 1424 trained via a machinelearning technique based on historical motion capture data 1420associated with one or more users 1402 performing one or more physicalactivities 1412, one or more commands 1426 for a second time interval1430 subsequent to the first time interval 1430. The model 1424 cangenerate the commands 1426 based in part on an activity 1412 the user1402 is performing or is about to perform. For example, differentactivities 1412 can include different commands 1426 to augment aparticular motion or movement of the user 1402 during the respectiveactivity 1412. The commands 1426 can include or correspond to one ormore torque profiles 1422 to be provided to the exoskeleton boot 100that include torque values 1428 for the exoskeleton boot 100 to apply toa lower limb of the user 1402 to augment or aid the user 1402 inperforming the subsequent or next activity 1412.

Embodiments of the subject matter and the operations described in thisspecification can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. The subject matter described inthis specification can be implemented as one or more computer programs,e.g., one or more circuits of computer program instructions, encoded onone or more computer storage media for execution by, or to control theoperation of, data processing apparatus. Alternatively or in addition,the program instructions can be encoded on an artificially generatedpropagated signal, e.g., a machine-generated electrical, optical, orelectromagnetic signal that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus. A computer storage medium can be, or be includedin, a computer-readable storage device, a computer-readable storagesubstrate, a random or serial access memory array or device, or acombination of one or more of them. Moreover, while a computer storagemedium is not a propagated signal, a computer storage medium can be asource or destination of computer program instructions encoded in anartificially generated propagated signal. The computer storage mediumcan also be, or be included in, one or more separate components or media(e.g., multiple CDs, disks, or other storage devices).

The operations described in this specification can be performed by adata processing apparatus on data stored on one or morecomputer-readable storage devices or received from other sources. Theterm “data processing apparatus” or “computing device” encompassesvarious apparatuses, devices, and machines for processing data,including by way of example a programmable processor, a computer, asystem on a chip, or multiple ones, or combinations of the foregoing.The apparatus can include special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application specificintegrated circuit). The apparatus can also include, in addition tohardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, across-platform runtime environment, a virtual machine, or a combinationof one or more of them. The apparatus and execution environment canrealize various different computing model infrastructures, such as webservices, distributed computing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a circuit, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more circuits,subprograms, or portions of code). A computer program can be deployed tobe executed on one computer or on multiple computers that are located atone site or distributed across multiple sites and interconnected by acommunication network.

Processors suitable for the execution of a computer program include, byway of example, microprocessors, and any one or more processors of adigital computer. A processor can receive instructions and data from aread only memory or a random access memory or both. The elements of acomputer are a processor for performing actions in accordance withinstructions and one or more memory devices for storing instructions anddata. A computer can include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto optical disks, or optical disks. Acomputer need not have such devices. Moreover, a computer can beembedded in another device, e.g., a personal digital assistant (PDA), aGlobal Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.Devices suitable for storing computer program instructions and datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto optical disks; and CD ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input.

The implementations described herein can be implemented in any ofnumerous ways including, for example, using hardware, software or acombination thereof. When implemented in software, the software code canbe executed on any suitable processor or collection of processors,whether provided in a single computer or distributed among multiplecomputers.

Also, a computer may have one or more input and output devices. Thesedevices can be used, among other things, to present a user interface.Examples of output devices that can be used to provide a user interfaceinclude printers or display screens for visual presentation of outputand speakers or other sound generating devices for audible presentationof output. Examples of input devices that can be used for a userinterface include keyboards, and pointing devices, such as mice, touchpads, and digitizing tablets. As another example, a computer may receiveinput information through speech recognition or in other audible format.

Such computers may be interconnected by one or more networks in anysuitable form, including a local area network or a wide area network,such as an enterprise network, and intelligent network (IN) or theInternet. Such networks may be based on any suitable technology and mayoperate according to any suitable protocol and may include wirelessnetworks, wired networks or fiber optic networks.

A computer employed to implement at least a portion of the functionalitydescribed herein may comprise a memory, one or more processing units(also referred to herein simply as “processors”), one or morecommunication interfaces, one or more display units, and one or moreuser input devices. The memory may comprise any computer-readable media,and may store computer instructions (also referred to herein as“processor-executable instructions”) for implementing the variousfunctionalities described herein. The processing unit(s) may be used toexecute the instructions. The communication interface(s) may be coupledto a wired or wireless network, bus, or other communication means andmay therefore allow the computer to transmit communications to orreceive communications from other devices. The display unit(s) may beprovided, for example, to allow a user to view various information inconnection with execution of the instructions. The user input device(s)may be provided, for example, to allow the user to make manualadjustments, make selections, enter data or various other information,or interact in any of a variety of manners with the processor duringexecution of the instructions.

The various methods or processes outlined herein may be coded assoftware that is executable on one or more processors that employ anyone of a variety of operating systems or platforms. Additionally, suchsoftware may be written using any of a number of suitable programminglanguages or programming or scripting tools, and also may be compiled asexecutable machine language code or intermediate code that is executedon a framework or virtual machine.

In this respect, various inventive concepts may be embodied as acomputer readable storage medium (or multiple computer readable storagemedia) (e.g., a computer memory, one or more floppy discs, compactdiscs, optical discs, magnetic tapes, flash memories, circuitconfigurations in Field Programmable Gate Arrays or other semiconductordevices, or other non-transitory medium or tangible computer storagemedium) encoded with one or more programs that, when executed on one ormore computers or other processors, perform methods that implement thevarious embodiments of the solution discussed above. The computerreadable medium or media can be transportable, such that the program orprograms stored thereon can be loaded onto one or more differentcomputers or other processors to implement various aspects of thepresent solution as discussed above.

The terms “program” or “software” are used herein to refer to any typeof computer code or set of computer-executable instructions that can beemployed to program a computer or other processor to implement variousaspects of embodiments as discussed above. One or more computer programsthat when executed perform methods of the present solution need notreside on a single computer or processor, but may be distributed in amodular fashion amongst a number of different computers or processors toimplement various aspects of the present solution.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Programmodules can include routines, programs, objects, components, datastructures, or other components that perform particular tasks orimplement particular abstract data types. The functionality of theprogram modules can be combined or distributed as desired in variousembodiments.

Also, data structures may be stored in computer-readable media in anysuitable form. For simplicity of illustration, data structures may beshown to have fields that are related through location in the datastructure. Such relationships may likewise be achieved by assigningstorage for the fields with locations in a computer-readable medium thatconvey relationship between the fields. However, any suitable mechanismmay be used to establish a relationship between information in fields ofa data structure, including through the use of pointers, tags or othermechanisms that establish relationship between data elements.

Any references to implementations or elements or acts of the systems andmethods herein referred to in the singular can include implementationsincluding a plurality of these elements, and any references in plural toany implementation or element or act herein can include implementationsincluding only a single element. References in the singular or pluralform are not intended to limit the presently disclosed systems ormethods, their components, acts, or elements to single or pluralconfigurations. References to any act or element being based on anyinformation, act or element may include implementations where the act orelement is based at least in part on any information, act, or element.

Any implementation disclosed herein may be combined with any otherimplementation, and references to “an implementation,” “someimplementations,” “an alternate implementation,” “variousimplementations,” “one implementation” or the like are not necessarilymutually exclusive and are intended to indicate that a particularfeature, structure, or characteristic described in connection with theimplementation may be included in at least one implementation. Suchterms as used herein are not necessarily all referring to the sameimplementation. Any implementation may be combined with any otherimplementation, inclusively or exclusively, in any manner consistentwith the aspects and implementations disclosed herein.

References to “or” may be construed as inclusive so that any termsdescribed using “or” may indicate any of a single, more than one, andall of the described terms. References to at least one of a conjunctivelist of terms may be construed as an inclusive OR to indicate any of asingle, more than one, and all of the described terms. For example, areference to “at least one of ‘A’ and ‘B’” can include only ‘A’, only‘B’, as well as both ‘A’ and ‘B’. Elements other than ‘A’ and ‘B’ canalso be included.

The systems and methods described herein may be embodied in otherspecific forms without departing from the characteristics thereof. Theforegoing implementations are illustrative rather than limiting of thedescribed systems and methods.

Where technical features in the drawings, detailed description or anyclaim are followed by reference signs, the reference signs have beenincluded to increase the intelligibility of the drawings, detaileddescription, and claims. Accordingly, neither the reference signs northeir absence have any limiting effect on the scope of any claimelements.

The systems and methods described herein may be embodied in otherspecific forms without departing from the characteristics thereof. Theforegoing implementations are illustrative rather than limiting of thedescribed systems and methods. Scope of the systems and methodsdescribed herein is thus indicated by the appended claims, rather thanthe foregoing description, and changes that come within the meaning andrange of equivalency of the claims are embraced therein.

What is claimed is:
 1. A system to augment motion via a battery-poweredactive exoskeleton boot, comprising: a shin pad of an exoskeleton bootto couple to a shin of a user below a knee of the user; one or morehousings enclosing i) a controller comprising memory and one or moreprocessors, and ii) an electric motor that generates torque about anaxis of rotation of an ankle joint of the user, wherein at least one ofthe one or more housings is located below the knee of the user andcoupled to the shin pad; a battery holder coupled to the shin pad, thebattery holder to receive a battery module; an output shaft coupled tothe electric motor and extending through a bore in a housing of the oneor more housings enclosing the electric motor; and the controller to:receive sensor data associated with activity of the exoskeleton bootduring a first time interval; input the sensor data into a model trainedvia a machine learning technique using historical motion capture dataassociated with one or more users performing one or more physicalactivities; generate a candidate torque profile based on the sensor datainput into the machine learning model; modify the model based on acomparison of the candidate torque profile and a target torque profilegenerated prior to receiving the sensor data; determine, for a secondtime interval subsequent to the first time interval, one or morecommands to match the candidate torque profile; and transmit the one ormore commands generated based on the model to the electric motor tocause the electric motor to generate torque about the axis of rotationof the ankle joint of the user in the second time interval.
 2. Thesystem of claim 1, comprising the controller to: receive, via a network,the model from a command modelling system that trains the model based onthe historical motion capture data.
 3. The system of claim 1, comprisinga command modelling system to: receive historical video data associatedwith the one or more users performing the one or more physicalactivities; identify, based on historical video information, one or moretorque profiles corresponding to the one or more physical activities;and train, using the machine learning technique and based on the one ormore torque profiles, the model to cause the model to output the one ormore commands responsive to the sensor data.
 4. The system of claim 1,comprising a command modelling system to: receive the historical motioncapture data comprising historical sensor data; provide, for display viaa display device communicatively coupled to the command modellingsystem, a visual indication of the historical motion capture data;receive, via a user interface, an indication of the candidate torqueprofile corresponding to the visual indication of the historical motioncapture data; and train, using the machine learning technique and basedon the indication of the candidate torque profile received via the userinterface, the model to cause the model to output the one or morecommands responsive to the sensor data.
 5. The system of claim 1,comprising a command modelling system to: receive the historical motioncapture data comprising historical sensor data; provide, for display viaa display device communicatively coupled to the command modellingsystem, a visual indication of the historical motion capture data;receive, via a user interface, an indication of a type of physicalactivity corresponding to the visual indication of the historical motioncapture data; and train, using the machine learning technique and basedon the indication of the type of physical activity received via the userinterface, the model to cause the model to output the one or morecommands responsive to the sensor data.
 6. The system of claim 5,wherein the type of physical activity comprises at least one of:walking, running, standing, standing up, or sitting.
 7. The system ofclaim 1, wherein the one or more physical activities comprise at leastone of steady state activities or transient activities.
 8. The system ofclaim 1, comprising the controller to: determine the one or morecommands for the second time interval to match the candidate torqueprofile selected based on the sensor data via the model.
 9. The systemof claim 1, comprising a command modelling system to: receive thehistorical motion capture data comprising historical sensor data;receive indications of types of physical activities corresponding to thehistorical motion capture data; and train, using a second machinelearning technique and based on the indications of types of physicalactivities corresponding to the historical motion capture data, a secondmodel to generate a second torque profile based on second historicalmotion capture data.
 10. The system of claim 9, comprising the commandmodelling system to: receive the second historical motion capture data;determine, based on the second model, one or more torque profiles basedon the second historical motion capture data; and train the model basedon the determined one or more torque profiles and the second historicalmotion capture data to cause the model to generate the one or morecommands based on the sensor data.
 11. The system of claim 1, whereinthe historical motion capture data corresponds to data collected via theexoskeleton boot in a plurality of states comprising: an unpoweredstate, a partially powered state, and a fully powered state.
 12. Thesystem of claim 1, comprising the controller to: receive, via a userinterface, input from the user prior to the second time interval; andgenerate, via the model, the one or more commands based on the input andthe sensor data.
 13. The system of claim 1, wherein the sensor datacomprises at least one of ankle joint data, inertial measurement unitdata, or battery data.
 14. The system of claim 1, wherein the historicalmotion capture data comprises at least one of inertial measurement unitdata, goniometer data, infrared reflector data, or force plate data. 15.A method of augmenting motion via a battery-powered active exoskeletonboot, comprising: providing a shin pad of an exoskeleton boot forcoupling to a shin of a user below a knee of the user; providing one ormore housings enclosing i) a controller comprising memory and one ormore processors, and ii) an electric motor that generates torque aboutan axis of rotation of an ankle joint of the user, wherein at least oneof the one or more housings is located below the knee of the user andcoupled to the shin pad; providing a battery holder coupled to the shinpad, the battery holder to receive a battery module; providing an outputshaft coupled to the electric motor and extending through a bore in ahousing of the one or more housings enclosing the electric motor;receiving, by the controller, sensor data associated with activity ofthe exoskeleton boot during a first time interval; inputting, by thecontroller, the sensor data into a model trained via a machine learningtechnique using historical motion capture data associated with one ormore users performing one or more physical activities; generating, bythe controller, a candidate torque profile based on the sensor datainput into the machine learning model; modifying, by the controller, themodel based on a comparison of the candidate torque profile and a targettorque profile generated prior to receiving the sensor data;determining, by the controller, for a second time interval subsequent tothe first time interval, one or more commands to match the candidatetorque profile; and transmitting, by the controller, the one or morecommands generated based on the model to the electric motor to cause theelectric motor to generate torque about the axis of rotation of theankle joint of the user in the second time interval.
 16. The method ofclaim 15, comprising: receiving, by a command modelling system,historical video data associated with the one or more users performingthe one or more physical activities; identifying, by the commandmodelling system based on the historical video data, one or more torqueprofiles corresponding to the one or more physical activities; andtraining, by the command modelling system, using the machine learningtechnique and based on the one or more torque profiles, the model tocause the model to output the one or more commands responsive to thesensor data.
 17. The method of claim 15, comprising: receiving, by acommand modelling system, the historical motion capture data comprisinghistorical sensor data; providing, by the command modelling system, fordisplay via a display device communicatively coupled to the commandmodelling system, a visual indication of the historical motion capturedata; receiving, by the command modelling system via a user interface,an indication of the candidate torque profile corresponding to thevisual indication of the historical motion capture data; and training,by the command modelling system, using the machine learning techniqueand based on the indication of the candidate torque profile received viathe user interface, the model to cause the model to output the one ormore commands responsive to the sensor data.
 18. The method of claim 15,comprising: receiving, by a command modelling system, the historicalmotion capture data comprising historical sensor data; providing, by thecommand modelling system, for display via a display devicecommunicatively coupled to the command modelling system, a visualindication of the historical motion capture data; receiving, by thecommand modelling system via a user interface, an indication of a typeof physical activity corresponding to the visual indication of thehistorical motion capture data; and training, by the command modellingsystem, using the machine learning technique and based on the indicationof the type of physical activity received via the user interface, themodel to cause the model to output the one or more commands responsiveto the sensor data.
 19. The method of claim 15, comprising: determining,by the controller, the one or more commands for the second time intervalto match the candidate torque profile selected based on the sensor datavia the model.
 20. The method of claim 15, comprising: receiving, by thecontroller via a user interface, input from the user prior to the secondtime interval; and generating, by the controller via the model, the oneor more commands based on the input and the sensor data.